Systems and techniques for tracking sleep consistency and sleep goals

ABSTRACT

Methods, techniques, apparatuses, and systems for setting up and tracking sleep consistency goals of users are provided. In one example, a computing system for setting a sleep schedule of a user of a biometric monitoring device may obtain sleep data derived from sensor data generated by the biometric monitoring device, store the sleep data in a sleep log data store as one or more sleep logs associated with an account assigned to the user, and calculate a target bedtime based on a scheduled waketime of the user and a sleep efficiency derived, at least in part, from the sleep data for one or more users stored in the sleep log data store. The computing system may also be configured to provide a number of personalized user interfaces to an individual for the purposes of setting a sleep schedule. Such interfaces may include parameters that are tailored to the individual sleep needs and/or characteristics of the individual&#39;s sleep.

CROSS-REFERENCE TO RELATED APPLICATION

This application is a continuation application under 35 U.S.C. § 120 ofU.S. patent application Ser. No. 15/171,049, filed Jun. 2, 2016, nowU.S. Pat. No. 10,325,514, issuing Jun. 18, 2019, which is herebyincorporated by reference in its entirety for all purposes.

BACKGROUND

Personal fitness and health monitoring devices, which may be referred toas biometric monitoring devices herein, may include a variety ofdifferent sensors that are used to provide feedback regarding variousphysiological characteristics of a person. Such sensors may be used totrack sleep of a user.

SUMMARY

Details of one or more implementations of the subject matter describedin this specification are set forth in the accompanying drawings and thedescription below. Other features, aspects, and advantages will becomeapparent from the description, the drawings, and the claims.

In one embodiment, a method of generating one or more personalizedgraphical user interfaces for setting a sleep schedule of a user of abiometric monitoring device may be provided. The method may includeobtaining sleep data derived from sensor data generated by the biometricmonitoring device, the sleep data including data regarding a pluralityof sleep sessions and specifying various sleep states of the user forthe respective sleep sessions; storing the sleep data in a sleep logdata store as one or more sleep logs associated with an account assignedto the user, the sleep log data store also including sleep logsincluding sleep data derived from sensor data generated by otherbiometric monitoring devices of other users; and causing the one or morepersonalized graphical user interfaces to be displayed to the user.Causing the one or more personalized graphical user interfaces to bedisplayed to the user may include obtaining a scheduled waketime of theuser, obtaining a recommended bedtime based on a calculation accountingfor, at least in part, the scheduled waketime and a sleep efficiencybased on the sleep data for one or more users stored in the sleep logdata store, and causing the recommended bedtime to be displayed in agraphical user interface element of at least one of the one or morepersonalized graphical user interfaces.

In some embodiments, causing the one or more personalized graphical userinterfaces to be generated may further include obtaining a selectedsleep duration of the user, and calculating the recommended bedtime maybe further based on the selected sleep duration of the user.

In some embodiments, causing the one or more personalized graphical userinterfaces to be generated may further include obtaining timinginformation indicating one or more selected reminder times for a bedtimereminder, and the method may further include generating the bedtimereminder based on the timing information.

In some further embodiments, the bedtime reminder may includeinformation regarding the recommended bedtime.

In some other further embodiments, causing the one or more personalizedgraphical user interfaces to be generated may further includecalculating a recommended reminder time for the bedtime reminder basedon the recommended bedtime and causing the recommended reminder time tobe displayed in a graphical user interface element of the one or morepersonalized graphical user interfaces.

In some other further embodiments, causing the one or more personalizedgraphical user interfaces to be generated may further include obtainingday information indicating one or more selected reminder days for thebedtime reminder, and the generating of the bedtime reminder may befurther based on the day information.

In some other further embodiments, the method may further includeproviding the bedtime reminder via a notification such as a message, anauditory output, an electronic communication, an electromagneticcommunication, a visual output, or a tactile output.

In some embodiments, causing the one or more personalized graphical userinterfaces to be generated may further include determining whether sleepstate duration data of the user that is representative of the time theuser spent in one or more non-awake sleep states during one of the sleepsessions of the user is within a threshold of the selected sleepduration, and causing information associated with the determination tobe displayed in a graphical user interface element of at least one ofthe one or more personalized graphical user interfaces.

In some further embodiments, causing the information associated with thedetermination to be displayed may include causing a timeline to bedisplayed showing a graphical indication of the threshold relative tothe timeline and a graphical indication of the sleep state duration datarelative to the timeline.

In some other further embodiments, causing the one or more personalizedgraphical user interfaces to be generated may further include causing,based on a determination that the sleep state duration data of the useris not within the threshold of the selected sleep duration, one or moreof: a second recommended bedtime, a recommended sleep duration, and arecommended waketime to be displayed in a graphical user interfaceelement of at least one of the one or more personalized graphical userinterfaces.

In some other further such embodiments, the one or more of the secondrecommended bedtime, the recommended sleep duration, and the recommendedwaketime that are displayed in the graphical user interface element maybe, respectively, different from the recommended bedtime by a first timeincrement, different from the selected sleep duration by a second timeincrement, and different from the scheduled waketime by a third timeincrement.

In some embodiments, causing the one or more personalized graphical userinterfaces to be generated may further include calculating a recommendedwaketime based on waketimes derived from the sleep data for the userstored in the sleep log data store and causing the recommended waketimeto be displayed in a graphical user interface element of at least one ofthe one or more personalized graphical user interfaces.

In some further embodiments, obtaining the recommended bedtime may befurther based on the recommended waketime.

In some embodiments, causing the one or more personalized graphical userinterfaces to be generated may further include calculating a recommendedsleep duration based on the sleep data for one or more users stored inthe sleep log data store, and causing the recommended sleep duration tobe displayed in a graphical user interface element of at least one ofthe one or more personalized graphical user interfaces.

In some further embodiments, obtaining the recommended bedtime may befurther based on the recommended sleep duration.

In some embodiments, causing the one or more personalized graphical userinterfaces to be generated may further include determining, based on thesleep data of the user, whether a waketime of the user for one of thesleep sessions of the user is within a threshold of the scheduledwaketime, and causing information associated with the determination tobe displayed in a graphical user interface element of at least one ofthe one or more personalized graphical user interfaces.

In some further embodiments, causing the information associated with thedetermination to be displayed may include causing a timeline to bedisplayed showing a graphical indication of the threshold relative tothe timeline and a graphical indication of the waketime relative to thetimeline.

In some other further embodiments, causing the one or more personalizedgraphical user interfaces to be generated may further include causing,based on a determination that the waketime of the user is not within thethreshold of the scheduled waketime, one or more of a second recommendedbedtime, a recommended sleep duration, and a recommended waketime to bedisplayed in a graphical user interface element of at least one of theone or more personalized graphical user interfaces.

In some other further such embodiments, the one or more of the secondrecommended bedtime, the recommended sleep duration, and the recommendedwaketime that are displayed in the graphical user interface element maybe, respectively, different from the recommended bedtime by a first timeincrement, different from the selected sleep duration by a second timeincrement, and different from the scheduled waketime by a third timeincrement.

In some embodiments, causing the one or more personalized graphical userinterfaces to be generated may further include determining, based on thesleep data of the user, whether a bedtime of the user for one of thesleep sessions of the user is within a threshold of the scheduledbedtime, and causing information associated with the determination to bedisplayed in a graphical user interface element of at least one of theone or more personalized graphical user interfaces.

In some further embodiments, causing the information associated with thedetermination to be displayed may include causing a timeline to bedisplayed showing a graphical indication of the threshold relative tothe timeline and a graphical indication of the bedtime relative to thetimeline.

In some other further embodiments, causing the one or more personalizedgraphical user interfaces to be generated may further include causing,based on a determination that the bedtime of the user is not within thethreshold of the scheduled bedtime, one or more of a second recommendedbedtime, a recommended sleep duration, and a recommended waketime to bedisplayed in a graphical user interface element of at least one of theone or more personalized graphical user interfaces.

In some other further embodiments, the one or more of the secondrecommended bedtime, the recommended sleep duration, and the recommendedwaketime that are displayed in the graphical user interface element maybe, respectively, different from the recommended bedtime by a first timeincrement, different from the selected sleep duration by a second timeincrement, and different from the scheduled waketime by a third timeincrement.

In some embodiments, the sleep efficiency may be representative, atleast in part, of a correlation between sleep state duration data forone or more sleep sessions stored in the sleep log data store and sleepsession duration data for the corresponding plurality of sleep sessions,the sleep state duration data may be representative of the total amountof time the user that is associated with the sleep session spent in asubset of non-awake sleep states during the sleep session, and the sleepsession duration data may be representative of the total duration of thesleep session.

In some embodiments, the method may further include, prior to obtainingthe recommended bedtime and causing the recommended bedtime to bedisplayed in the graphical user interface element of the at least one ofthe one or more personalized graphical user interfaces determining thatthe user is a new user without sleep data stored in the sleep log datastore, and causing a suggested sleep duration to be displayed in agraphical user interface element of at least one of the one or morepersonalized graphical user interfaces. The suggested sleep duration maybased on one or more of a default value and a target sleep durationdetermined based on one or more of the sleep logs including sleep dataderived from the sensor data generated by the other biometric monitoringdevices of the other users.

In some embodiments, the method may further include determining that afirst number of sleep logs associated with the account assigned to theuser exist in the sleep log data store, and the causing the one or morepersonalized graphical user interfaces to be displayed may occur afterthe determination.

In one embodiment, a system for setting a sleep schedule of a user of abiometric monitoring device may be provided. The system may include oneor more processors and a memory. The one or more processors may becommunicatively connected with the memory, and the memory may storeinstructions that, when executed, cause the one or more processors toobtain sleep data derived from sensor data generated by the biometricmonitoring device, the sleep data including data regarding a pluralityof sleep sessions and specifying various sleep states of the user forthe respective sleep sessions; store the sleep data in a sleep log datastore as one or more sleep logs associated with an account assigned tothe user, the sleep log data store also storing other sleep logsincluding other sleep data derived from other sensor data generated byother biometric monitoring devices of other users; and calculate atarget bedtime based on a scheduled waketime of the user and a sleepefficiency derived, at least in part, from the sleep data for one ormore users stored in the sleep log data store.

In some embodiments, the target bedtime may be based on the sleepefficiency of the other users of the other biometric monitoring devices.In some other embodiments, the target bedtime may be based on the sleepefficiency of the user of the biometric monitoring device.

In some embodiments, the memory may further store instructions that,when executed, cause the one or more processors to obtain sleep stateduration data for a sleep session stored in the sleep log data store,the sleep state duration data being representative of the total amountof time the user that is associated with the sleep session spent in oneor more sleep states during the sleep session, determine sleep sessionduration data for the sleep session stored in the sleep log data store,the sleep session duration data being representative of the total timeof that sleep session. The sleep efficiency may be representative, atleast in part, of a correlation between sleep state duration data forone or more sleep sessions and sleep session duration data for thecorresponding one or more sleep sessions.

In some further embodiments, the memory may further store instructionsfor controlling the one or more processors to obtain the scheduledwaketime of the user from the user via a graphical user interface.

In some other further embodiments, the calculation of the target bedtimemay be further based on the sleep efficiency of the user.

In some other further embodiments, the calculation of the target bedtimemay be further based on a selected sleep duration of the user for asleep session.

In some other further embodiments, the memory may further storeinstructions for controlling the one or more processors to obtain theselected sleep duration of the user for the sleep session.

In some such embodiments, the memory may further store instructions forcontrolling the one or more processors to determine a regression modelthat relates the sleep session duration data for a plurality of sleepsessions stored in the sleep log data store to corresponding sleep stateduration data for the plurality of sleep sessions, and the calculationof the target bedtime may be further based on the regression model.

In some further such embodiments, the sleep efficiency for each sleepsession may be calculated, at least in part, by dividing the sleep stateduration data for that sleep session by the sleep session duration datafor that sleep session.

In some other further such embodiments, the plurality of sleep sessionsmay be for the other users of the other biometric monitoring devices.

In some other further such embodiments, the regression model may be aregression model such as a linear regression model, a nonlinearregression model, a parametric regression model, a nonparametricregression model, a semiparametric regression model, and a multivariatelinear regression model.

In some other further such embodiments, the sleep session duration dataand corresponding sleep state duration data for the plurality of sleepsessions that may be included in the regression model are associatedwith sleep sessions with waketimes that are within a first thresholdamount of the scheduled waketime.

In some other further such embodiments, the sleep session duration dataand corresponding sleep state duration data for the plurality of sleepsessions that may be included in the regression model are associatedwith sleep state duration data that are within a second threshold amountof the selected sleep duration.

In some other further such embodiments, the sleep session duration dataand corresponding sleep state duration data for the plurality of sleepsessions that may be included in the regression model are associatedwith sleep sessions with waketimes that have occurred on the same day ofthe week as the day of the week on which the scheduled waketime occurs.

In some other further such embodiments, the regression model may accountfor one or more of one or more specific days of the week, a specifictime of year, holidays, workdays of the user, non-workdays of the user,a seasonal time change, a geographic location, travel by the userbetween at least two time zones, exercise of the user, and a duration ofdaylight in a day.

In some embodiments, the sleep efficiency for each sleep session may becalculated, at least in part, by dividing the sleep state duration datafor each sleep session by the sleep session duration data for eachcorresponding sleep session, respectively, and the sleep efficiency maybe accounted for, at least in part, by multiplying the selected sleepduration by a factor that is based on the sleep efficiencies for theplurality of sleep sessions to determine a predicted sleep sessionduration.

In some other embodiments, the memory may store instructions for furthercontrolling the one or more processors to cause a notification mechanismto produce a notification relating to a comparison of sleep stateduration data for one or more sleep sessions with the selected sleepduration data.

In some embodiments, the memory may store instructions for furthercontrolling the one or more processors to determine a recommendedbedtime based on the target bedtime. The recommended bedtime may befurther based on one or more of one or more specific days of the week, aspecific time of year, holidays, workdays of the user, non-workdays ofthe user, a seasonal time change, a geographic location, travel by theuser between at least two time zones, exercise of the user, and aduration of daylight in a day.

In some embodiments, the sleep efficiency may be further based on ofhistorical sleep efficiency data associated with one or more of a propersubset of one or more specific days of the week, a time of year,holidays, workdays of the user, non-workdays of the user, a seasonaltime change, a geographic location, travel by the user between at leasttwo time zones, exercise of the user, and a duration of daylight in aday.

In some embodiments, the system may further include the biometricmonitoring device that includes one or more sensors configured togenerate the sensor data, and a communications interface to communicatethe sensor data to the one or more processors.

BRIEF DESCRIPTION OF THE DRAWINGS

The various implementations disclosed herein are illustrated by way ofexample, and not by way of limitation, in the figures of theaccompanying drawings, in which like reference numerals refer to similarelements.

FIG. 1 depicts a high-level flow diagram of sleep scheduling and sleeptracking techniques discussed herein.

FIG. 2 depicts a high-level flow diagram that outlines techniques forobtaining a selected sleep duration of an individual.

FIG. 3 depicts a high-level flow diagram of techniques for obtaining ascheduled waketime.

FIG. 4 depicts a high-level flow diagram of techniques for obtaining aselected bedtime.

FIG. 5 is a block diagram of an example computational environment thatmay be used to implement the techniques and methods discussed herein.

FIG. 6 depicts a high-level schematic of a sleep tracking system.

FIG. 7 is an example graphical user interface for a user new to sleeptracking.

FIG. 8 is another example graphical user interface for a user new tosleep tracking.

FIG. 9 is an example of a graphical user interface that may be generatedfor a user new to sleep tracking.

FIG. 10 is an example of a graphical user interface that may begenerated in order to allow a user to provide the amount of sleep thatthey believe they typically attain each night.

FIG. 11 is an example of a graphical user interface that may be used toconfirm whether a selected sleep duration for a user is sufficient.

FIG. 12 is an example of a graphical user interface that may bepresented to a user if the user selects a selected sleep duration thatis less than a recommended amount of sleep duration.

FIG. 13 is an example of a graphical user interface that may bepresented to a user if the user previously indicated that they wouldlike to increase their selected sleep duration.

FIG. 14 is an example of a graphical user interface that may bepresented to a user in order to allow the user to change their selectedsleep duration.

FIG. 15 is an example of a graphical user interface that may be used toprovide instructions to a user of a wearable biometric tracking devicewith manually activated sleep tracking.

FIG. 16 is an example of a graphical user interface that may be used toprovide instructions to a user of a wearable biometric tracking devicewith automatic sleep tracking.

FIG. 17 is an example of a graphical user interface that may be used toprovide instructions to a user once initial sleep tracking parametershave been established.

FIG. 18 is an example of a graphical user interface that may be used toconvey sleep performance data to a user.

FIG. 19 is an example of a graphical user interface that may be used tointroduce a user to sleep scheduling features.

FIG. 20 is an example of a graphical user interface that may be used topresent typical sleep duration of the user and inquire whether thatamount of sleep duration is sufficient.

FIG. 21 is another example of a graphical user interface that may beused to present typical sleep duration of the user and inquire whetherthat amount of sleep duration is sufficient.

FIG. 22 is an example of a graphical user interface that may be used topresent a recommended sleep duration and allow a user to confirm ormodify that recommended sleep duration.

FIG. 24 is an example of a graphical user interface that may be used topresent a recommended sleep duration to the user and to inquire whetherthat amount of sleep duration is sufficient.

FIG. 23 is an example of a graphical user interface that may be used toallow the user to update a selected sleep duration.

FIG. 25 is an example of a graphical user interface that may be used tointroduce a user to the concept of a sleep schedule.

FIG. 26 is an example of a graphical user interface that may be used topresent the user's typical waketime to the user and to inquire whetherthat waketime is acceptable as a scheduled waketime.

FIG. 27 is an example of a graphical user interface that may be used toallow the user to adjust their scheduled waketime.

FIG. 28 is an example of a graphical user interface that may be used toallow the user to indicate whether or not an alarm should be set to wakethe user up at the scheduled waketime.

FIG. 29 is a plot of an example linear regression model fit to aplurality of sleep data points.

FIG. 30 is a plot of an example non-linear regression model fit to theplurality of sleep data points of FIG. 29.

FIG. 31 is an example of a graphical user interface that may be used torecommend a personalized recommended bedtime to a user and to allow theuser to confirm or reject the recommended bedtime for use as a selectedbedtime.

FIG. 32 is an example of a graphical user interface that may be used toallow the user to adjust the selected bedtime.

FIG. 33 is an example of a graphical user interface that may be used toestablish a reminder in advance of a selected bedtime.

FIG. 34 is an example of a graphical user interface that may be used toallow the user to change timing information for a bedtime reminder.

FIG. 35 is an example of a graphical user interface that may be used toallow a user to specify on which days to provide a bedtime reminder.

FIG. 36 is an example of a graphical user interface that may be used toprovide summary information regarding sleep data and to compare sleepdata against desired sleep goals.

FIG. 37 is another example of a graphical user interface that may beused to provide summary information regarding sleep data and to comparesleep data against desired sleep goals.

FIG. 38 is an example of a graphical user interface that may be used toallow a user to edit sleep scheduling parameters.

DETAILED DESCRIPTION

An aspect of the present disclosure relates to assisting and guidingusers to set goals that promote consistent sleep behavior. Consistentsleep behavior is a foundation of healthy sleep due in part to itsrelation to the circadian rhythm which regulates many bodily functions,including sleep. For example, this rhythm affects when a person isawake, aware, tired, and ready to sleep. The more regular a person'ssleep cycles are, the easier it may be for the person's body to functionin accordance with the circadian rhythm. Consistent sleep behavior,which may include a consistent waketime, bedtime, and/or sleep duration,helps establish this rhythm and keep a person's sleep well patterned andhealthy. The present disclosure is directed to methods, techniques,apparatuses, systems, and the like which help guide a user to set goalsfor sleep duration, wakeup time, and/or bedtime that will in turn helpestablish consistent sleep behaviors. Some of the techniques discussedin this disclosure utilize “sleep tracking,” which includes themonitoring, i.e., tracking, of a user's sleep by, at least in part,analyzing sleep data that is derived from sensor data generated by abiometric monitoring device, in order to better guide a person to a moreconsistent sleep cycle. The present inventors have determined that themethods, techniques, apparatuses, systems, and the like disclosed hereinmay, in some cases, be used to promote better sleep consistency andimprove the sleep of users.

As noted above, one factor for consistent sleep behavior is sleepduration of the user for a given sleep session; however, each persondoes not require the same sleep duration. For example, while a typicalsleep duration recommendation for adults is 7-9 hours of sleep,different people may need different sleep durations in order to behealthy, productive, and/or to feel rested. Therefore, the presentdisclosure is intended, at least in part, to determine, recommend,and/or set an individualized or personalized recommended sleep durationfor each user. Further, some of the embodiments described herein mayprovide recommended sleep duration with higher fidelity as compared tothe two hour window provided by a generic recommendation of 7-9 hours ofsleep. For example, the embodiments described herein may generate asleep duration recommendation that is, in some cases, a range ofdurations (e.g., 8+/−0.5 hours) or, in other cases, a specific duration(e.g., 8 hours).

A “sleep session,” as used herein, may generally be considered dataand/or logic that represents a period of time during which an individualis attempting to sleep or is actually sleeping. The precise data orlogic of a sleep session may vary depending on a variety of factors. Forexample, the start time and end time of a sleep session may beestablished according to a variety of different techniques. For example,the start time of a sleep session may be derived from sensors when anindividual first enters a bed in an attempt to sleep (such as may bemeasured by a biometric monitoring device that includes a pressuresensor located in a bed—if the sensor detects pressure commensurate withthe presence of the individual, then the biometric monitoring device maydetermine that the individual in bed and is attempting to sleep), whenthe individual manually indicates to a biometric monitoring device thebeginning of a sleep session (such as by pressing a button on abiometric monitoring device to indicate the start of a sleep session),and/or when a processor has determined, from data generated by, forinstance, the biometric monitoring device, that a person has engaged inbehavior indicative of the start of a sleep session (e.g., by remaininggenerally motionless for an extended period of time, such as may bedetected by accelerometers or other motion sensors in the biometricmonitoring device).

In some cases, the start of a sleep session may be contemporaneous ornear-contemporaneous with when a person actually falls asleep. Forexample, if data from a wearable biometric monitoring device worn by anindividual indicates that the person has transitioned from an awakestate to an asleep state (without any data indicating that theindividual is trying to go to sleep beforehand), then such a transitionmay be deemed to be the start or beginning of the sleep session.Similarly, for example, the sleep session may end when a person exits abed, when the individual manually inputs to the biometric monitoringdevice the end of the sleep session, and/or when the processor hasdetermined, from data generated by the biometric monitoring device, thatthe person has stopped sleeping (for example, accelerometers in thebiometric monitoring device indicate that the person is walking orotherwise engaged in prolonged movement). In some embodiments, eachsleep session may be bounded by a respective bedtime of the user andrespective waketime of the user. It is to be understood that a sleepsession may be punctuated by periods of wakefulness or restlessness—forexample, an individual may get up to go to the bathroom, let a pet out,or nurse a baby. Such short periods of wakefulness, when followed by areturn to sleep, would not be viewed as the “end” of a sleep session. Aswill be clear, the exact start and end times of a sleep session may besubject to some variability depending on what criteria are used todetermine such parameters; it is to be understood that the concepts andtechniques discussed herein may be practiced using any of a variety ofsuch different definitions of what a sleep session is. In the context ofcomputing or processing systems that be used to implement the techniquesand methods discussed herein and to collect physiological data, e.g.,heart rate, movement, breathing rate, etc., of an individual duringsleep and log such data, the term “sleep session” is to be understood torefer to the window of time that such a computing or processing systemestablishes as being representative of the individual's actual sleepsession. The duration of a sleep session, which may include periods ofboth sleep and wakefulness, as described above, is referred to herein asthe “sleep session duration.”

The biometric monitoring devices that may be used to collect biometricsensor data, e.g., physiological data, that may be evaluated todetermine one or more types of sleep data usable with the techniquesdiscussed herein may be wearable biometric monitoring devices, e.g.,such as a wrist-wearable fitness tracker such as the Fitbit™ Charge™,Surge™, or other similar device; non-wearable biometric monitoringdevices, such as sleep-monitoring sensor systems that are integratedinto a mattress pad, mattress, or similar bed-integrated system or thatare configured to operate from a location remote from a bed, e.g., suchas a nightstand adjacent to a bed; or combinations thereof.

As an individual sleeps or prepares to sleep, the embodiments discussedherein may detect that the individual passes through a variety ofdifferent states, such as an awake state, an asleep state, a wakingstate (in which the individual is transitioning from an asleep state toan awake state), etc. At a minimum, the embodiments may determine thatan individual is in one of at least two states, “awake” and “asleep,” atone or more given times during a sleep session. In some cases,additional determinations may be made to identify when an individual isin one or more other sleep states, e.g., a restless state, a waking orstarting-to-wake state, etc. Moreover, in some implementations, sleepstates may be further granularized into multiple sleep stages or sleepphases. For example, an asleep state may include one or more periodswhen the individual is in stage 1, e.g., NREM1, N1, somnolence, light,or drowsy sleep) sleep, one or more further periods where the individualis in stage 2, e.g., NREM2 or N2) sleep, one or more further periodswhere the individual is in stage 3, e.g., NREM3, N3, deep, delta, orslow-wave sleep) sleep, and one or more periods of random eye movement(REM) sleep; it is not necessarily the case that an individual will passthrough all of these stages of sleep in any given sleep session—each ofthese sleep stages may also be viewed as a type of sleep state or aspart of a sleep state (if a sleep state includes time spent in multiplesleep stages). An individual's sleep duration is a characterization ofthe amount of time that that individual spends in one or more sleepstates other than the awake state during a sleep session. The sleepstates of interest for determining sleep duration (which may also bereferred to herein as “sleep state duration”) may include sleep statesthat represent one or more of the different stages of sleep. In somecases, sleep duration may simply be a measure of how much time a givenindividual is asleep during a sleep session—regardless of whether theindividual is experiencing restless, light, deep, or REM sleep. In othercases, such a system may be configured to only credit time spent in deepor REM sleep towards an individual's sleep duration. Thus, sleep stateduration may, in some implementations, be inclusive of all of the timethat a user spent in any of the non-awake sleep states. The sleep stateduration may, in other implementations, be inclusive of all of the timethat a user spent in a non-empty proper subset of the non-awake sleepstates.

As noted above, the user's sleep duration, including the time spent invarious sleep states, as well as other sleep-related information aboutthe user, may be determined and/or obtained from sleep data derived fromsensor data generated by a biometric monitoring device. For example, thebiometric monitoring device may include a plurality of sensors which areconfigured to generate data indicative of the movements andphysiological state of the user; when the user wears the biometricmonitoring device to bed, at least some of the biometric monitoringdevice's sensors may generate data that is collected and analyzed by oneor more processors to derive sleep data from this sensor data. Thesensors collecting such data may be, for instance, a motion sensor likea multi-axis accelerometer or an optical heart rate monitor moduleincluding a photoplethysmographic sensor. The generated sensor data mayinclude, for instance, movement data of the user or heart rates of theuser. The generated sensor data may be analyzed and/or categorized intosleep data, which may include, for example, the sleep duration, sleepstates, sleep stages, sleep state duration, sleep session duration,waketime, and/or bedtime of the user for each sleep session—for example,various Fitbit™ biometric monitoring devices, such as the Charge™,Charge HR™, Pulse™, utilize accelerometer data to track a person'smovement during sleep and identify periods or epochs during which theperson is awake, restless, or awake. Sleep data may also include variousbiometric measurements that, while not directly related to sleep, maynonetheless be useful in determining various sleep-related data, e.g.,heart rate (heart rate may indicate, in part, a particular sleep stateor sleep stage of a person), respiratory rate (respiratory rate may alsoindicate, in part, a particular sleep state or sleep stage of a person),movement levels, etc. Based on this data, a total sleep sessionduration, total amount of time actually spent asleep, and total amountof time spent awake or restless may be determined. For example, for agiven sleep session, the sleep data for that session may indicate thatthe individual went to bed at 11:06 PM and woke up at 6:33 AM (for asleep session duration of 7 hours and 27 minutes), was restless 15 timesduring the night, woke up 3 times, spent 37 minutes total in an awake orrestless state, and was actually asleep for 6 hours and 50 minutes. Itis to be understood that “based on,” in the context of this disclosure,also means “based, at least in part, on,” and that these two phrases maybe used interchangeably. Moreover, it is to be understood that ininstances where “based on” or “based, at least in part, on” are used,one of the implementations that is embraced by such language, unless thecontext of such usage indicates otherwise, is the singular case, e.g.,where a determination of a parameter is “based exclusively on” aparticular type of data. For example, if a parameter A is calculatedbased on (or based, at least in part, on) a parameter B, this is to beunderstood as encompassing situations in which parameter A may becalculated solely based on parameter B as well as situations in whichparameter A may be calculated based on parameter B in combination withother parameters, such as parameter(s) C (and D).

For example, the biometric monitoring device's sensors may generatemovement data of the user which may be analyzed by one or moreprocessors to determine a user's sleep state, e.g., based on sleep stageor on other determinations without reference to a particular sleepstage, such as overall restlessness. One such analysis may include thegeneration of a movement measure or sleep coefficient based on, at leastin part, the movement data that is collected for a period of time, e.g.,30 seconds. Then, based on the movement measure of a larger period oftime, e.g., a window of time larger than the period of time, an activitylevel of the user is calculated; the activity level may be active orinactive. A user's sleep state may then be classified based, at least inpart, on the activity levels of the user calculated for a contiguoustime period over a number of windows of time.

A further example of obtaining, measuring, and/or determining sleepdata, such as a user's sleep duration and sleep states, is more fullydescribed in U.S. patent application Ser. No. 14/859,192, filed Sep. 18,2015, titled “Movement Measure Generation In A Wearable ElectronicDevice”, which is hereby incorporated herein by reference for allpurposes.

Some techniques of the present disclosure include generating one or morepersonalized graphical user interfaces for setting a sleep schedule of auser of a biometric monitoring device. At least some of the graphicaluser interfaces may be generated in a display of the biometricmonitoring device while at least some others may be generated in adisplay of an electronic device such as a smartphone, tablet, and/orpersonal computer.

As noted earlier, the techniques and systems discussed herein may beused to guide users of biometric monitoring devices to engage in moreconsistent sleep behavior.

FIG. 1 depicts a high-level flow diagram of sleep scheduling and sleeptracking techniques as discussed in more detail below. As can be seen inFIG. 1, the overall technique may involve obtaining, through variousmechanisms, one or more parameters affecting an individual's sleepschedule—for example, obtaining one or more of a selected sleep duration101, a scheduled waketime 102, and a selected bedtime 103 may beobtained through one or more different techniques, as discussed in moredetail below. After one or more of such parameters have been obtained,the individual's sleep may be tracked 104 to determine variouscharacteristics of the person's sleep behaviors, which may be stored assleep data. The sleep data may be used for a variety of purposes,including for presentation 105 to the individual and for providingpersonalized recommended bedtimes, as discussed in more detail later.

The various sleep parameters that are obtained, e.g., selected sleepduration, scheduled waketime, and selected bedtime, may be obtainedrelatively simultaneously, e.g., by a user entering all three parametersinto a common user interface (see, for example, the GUI of FIG. 38,which is discussed later), or in a staged or staggered manner. Forexample, for an individual who is new to sleep tracking or who has notpreviously set up sleep tracking on a biometric monitoring device, aselected sleep duration may first be obtained and then the individual'ssleep may then be tracked in order to determine how consistently theindividual is meeting such a sleep duration goal—at some later point,further sleep parameters may be obtained, such as the individual'sscheduled waketime and selected bedtime. Such a staged progression mayallow for the gradual introduction of the individual to the concept ofsleep tracking and scheduling, and may serve to be a more effectivemechanism for encouraging more consistent sleep behavior. Alternatively,if a user waits for some period of time before exploring the sleeptracking functionality of their biometric monitoring device, thebiometric monitoring device may nonetheless collect sleep data for theuser if the user wears it bed. Thus, when the user initially exploresthe sleep tracking functionality, the biometric monitoring device mayalready have collected some initial sleep data that may be used tosuggest a selected sleep duration that is based on the user's actualsleep behavior. In a further variation, a user's past sleep data thatwas collected with another biometric monitoring device may be used inplace of sleep data collected with the biometric monitoring device beingset up, and a selected sleep duration (and other parameters) may beprovided based on such earlier-collected sleep data.

FIG. 2 depicts a high-level flow diagram that outlines techniques forobtaining a selected sleep duration of an individual. To start with, aninitial selected sleep duration may be obtained, e.g., by querying theindividual to enter a typical sleep duration (as estimated and reportedby the individual) in block 201 or by analyzing historical sleep data ofthe individual in block 202 to determine, for example, an average sleepduration for the individual for some period of time. Once an initialselected sleep duration has been obtained, it may optionally be useddirectly as the selected sleep duration or may, for example, be subjectto modification. For example, the initial selected sleep duration may bepresented to the individual and the individual may be prompted in block203 to indicate if the initial selected sleep duration is acceptable orif the individual wishes to change it. If the individual indicates thatthe initial selected sleep duration is acceptable, then the initialselected sleep duration may be used as the selected sleep duration inblock 205. If the individual indicates the initial selected sleepduration is not acceptable, then the individual may be queried in block204 for an alternate selected sleep duration. Once the individual haseither confirmed the initial selected sleep duration as the selectedsleep duration or selected an alternate selected sleep duration, theselected sleep duration may be considered to be obtained in block 205.

As mentioned earlier, an individual's actual sleep duration may betracked, e.g., via sleep data obtained from a biometric monitoringdevice, in order to determine to what degree the individual's actualsleep duration correlates with the individual's selected sleep duration.In some implementations, the individual may be prompted to re-evaluatetheir selected sleep duration to determine if a more achievable selectedsleep duration should be selected—for example, if the individual isconsistently sleeping only 6 hours a night but has a selected sleepduration of 7 hours, then the individual may be prompted to select alower selected sleep duration initially. If the individual's sleep datathen indicates a subsequent increase in the individual's actual sleepduration, then the individual may be intermittently encouraged to raisethe selected sleep duration, e.g., by 15 minutes or 30 minutes at atime, in order to gradually reach the original selected sleep duration.

Once a selected sleep duration for an individual has been obtained, suchinformation may eventually be used with an obtained scheduled waketimeand collected sleep data in order to provide a recommended bedtime.

FIG. 3 depicts a high-level flow diagram of techniques for obtaining ascheduled waketime. To begin with, the individual may be queried inblock 301 to provide a typical waketime, e.g., the time at which theindividual normally wakes up, or, alternatively (or additionally), theindividual may be presented in block 302 with a typical waketime, e.g.,an average waketime, as determined from historical sleep data of theindividual. After the typical waketime is obtained, the typical waketimemay optionally be presented to the individual and the individualprompted to confirm whether or not the typical waketime is acceptable tothe individual in block 303. If the individual indicates in block 303that the typical waketime is not acceptable in block 303, the individualmay be prompted to select an alternate scheduled waketime in block 304,which may then be used as the scheduled waketime in block 306. If theindividual indicates in block 303 that the typical waketime isacceptable, then the typical waketime may be used as the scheduledwaketime in block 305. Once the scheduled waketime has been obtained,then, in some implementations, the technique may optionally also includeconfiguring a waketime alarm. For example, in block 306, the individualmay be presented with a query as to whether or not the individual wishesto set a waketime alarm. If the individual indicates that they wish toset a waketime alarm in block 306, a waketime alarm, e.g., an audiblealarm on a smartphone or a vibratory alarm on a wearable biometricmonitoring device, may be set to activate at the scheduled waketime inblock 307 before the waketime alarm settings may be considered obtainedin block 308. If the individual indicates that no waketime alarm is tobe set in block 306, then the waketime alarm settings may be consideredto be obtained (with no alarm set) in block 308.

Once a scheduled waketime and a selected sleep duration have beenobtained, a selected bedtime may be obtained. FIG. 4 depicts ahigh-level flow diagram of techniques for obtaining a selected bedtime.To obtain a selected bedtime, a target bedtime may first be determined,taking into account the sleep efficiency of the individual (or of otherindividuals), in block 401; such a determination may also be based onthe selected sleep duration and the scheduled waketime. Once the targetbedtime has been determined, a recommended bedtime may be optionallydetermined in block 402 based on the target bedtime, as discussed inmore detail below—the recommended bedtime may, for example, be modifiedfrom the target bedtime to be the closest 15-minute time on the hour tothe target bedtime (in other implementations, the target bedtime maysimply be used as the recommended bedtime). Once determined, therecommended bedtime may be presented to the individual in block 403 andthe individual may be requested to confirm if the recommended bedtime isacceptable to the individual as a selected bedtime in block 404. If theindividual indicates in block 404 that the recommended bedtime isacceptable, then the recommended bedtime may be used as the selectedbedtime in block 406. If the individual indicates in block 404 that therecommended bedtime is not acceptable as the selected bedtime, then theindividual may be prompted in block 405 to provide an alternate selectedbedtime in block 405, which may then be used as the selected bedtime inblock 406.

After a selected bedtime has been determined, the individual may beoptionally presented with an opportunity to set a bedtime reminder inblock 407. If the individual indicates in block 408 that no bedtimereminder is desired, then the bedtime reminder settings may beconsidered to have been obtained (with no reminder set) in block 412. Ifthe individual indicates in block 407 that a bedtime reminder isdesired, then initial bedtime reminder timing information may bepresented to the individual in block 408, after which the individual maybe presented in block 409 with an opportunity to indicate whether theinitial bedtime reminder timing information is acceptable. If so, thenthe technique may proceed to block 411 using the initial bedtimereminder timing information as the bedtime reminder setting. If not,then the technique may first query the individual to enter alternatebedtime reminder timing information in block 410 before proceeding toblock 412.

The techniques outlined above may be implemented in a variety of ways;the following discussion examines some of these implementations in thecontext of some example graphical user interfaces (GUIs). It is to beunderstood that GUIs may include a multitude of different graphical userinterface elements, e.g., fields, controls, images, charts, graphs,buttons, sliders, etc. that may be used to convey or receive data to orfrom a user. While discrete GUIs are discussed below, such discrete GUIsmay also be provided by simply replacing or updating a GUI element inone GUI to provide the information conveyed in another GUI (for example,the GUIs of FIGS. 12 and 13, which are discussed later, may be the sameGUI with the same GUI elements, but with different data placed in twofields—“Adults are recommended to get 7-9 hours but everyone isdifferent. For now, let's set your sleep goal to” is replaced by “Adultsare recommended to get 7-9 hours. For now, let's set your sleep goalto,” and “6 hr 30 min” is replaced by “7 hr 00 min”). Thus, in thediscussions below, it is to be understood that reference to providing orgenerating a GUI is inclusive of causing a GUI element of a GUI to actin a particular way or display particular data or information that is tobe conveyed via that GUI.

Computational Environment

The techniques and methods discussed herein are implementable in avariety of different computational environments involving a variety ofcomputing platforms and may make use of a number of different computingsystems. It is to be understood that the techniques and methodsdiscussed herein are not to be limited to a particular computationalenvironment, but may be implemented in a number of different specificways consistent with this disclosure, and that all of these differentimplementations are within the scope of this disclosure.

FIG. 5 depicts a block diagram of an example computational environmentthat may be used to practice the techniques and methods discussedherein. As can be seen, the computational environment may include aremote server or servers, e.g., a cloud-based computing system, that mayinclude one or more processors, memory (such as random-access memorythat may be used to store computer-executable instructions duringprogram execution and for temporary data storage), storage (such as harddisk arrays or other non-volatile storage media), and a communicationsinterface (such as a TCP/IP network connection or similar communicationsinterface). Such remote servers may be communicatively connected with aplurality of mobile communications devices, e.g., smartphones, tablets,etc., that may be owned and/or used by individuals owning and/or usingbiometric monitoring devices. The mobile communications devices may eachsimilarly have one or more processors, memory, storage, andcommunications interfaces that allow them to communicate with the remoteservers via a network. The communications interfaces of the mobilecommunications devices may also include a communications interface forcommunicating with the biometric monitoring devices, e.g., a Bluetoothcommunications interface or similar short-range, low-power link. Thebiometric monitoring devices may also have their own processors, memory,storage, and communications interfaces, as well as one or more biometricsensors that may be used to monitor activities of their users.

These various components or elements of the example computationalenvironment may be configured to individually or cooperatively performthe various techniques and methods discussed herein. For example, themobile communications device(s) may serve as an information relaybetween the remote server(s) and the biometric monitoring device(s), andmay also serve to provide the GUIs discussed herein using, for example,a GUI engine that includes logic or computer-executable instructions forcontrolling the one or more processors of the mobile communicationsdevice to provide the various GUIs discussed herein. The biometricmonitoring device(s) may, for example, collect data from the biometricsensors, store such collected data locally in the biometric monitoringdevice(s), and then upload the collected data to the remote server viathe mobile communications device(s). The biometric monitoring device orthe mobile communications device may also provide alarm functionality,e.g., the waketime and/or bedtime reminder alarms discussed laterherein, via an alarm engine that includes logic or computer-executableinstructions for controlling the one or more processors of such elementsto provide such alarms. The remote servers, for example, may providelong-term storage of collected sleep data, as well as performing sleepdata analysis, such as identifying periods spent in each sleep state,the timing of any waking events, etc., such as may be performed by asleep data analysis engine. Such sleep data analysis may alternativelyor additionally be performed by the mobile communications device and/orthe biometric monitoring device, depending on the particulararchitecture that is implemented. A bedtime recommendation, as discussedlater herein, may also be provided by a bedtime recommendation engine,which may include computer-executable instructions or logic forcontrolling one or more of the processors of one or more of the elementsdiscussed above to determine a bedtime recommendation according to thetechniques discussed herein.

The particular example depicted in FIG. 5 is only one representativeexample of a system and computational environment that may be used toimplement the techniques and methods discussed herein, and it is to beunderstood that other systems and computational environments that mayprovide one or more aspects of the methods and techniques discussedherein are also considered within the scope of this disclosure.

Sleep Data Collection and Storage

As noted earlier, sleep data that is derived from sensor data that isgenerated by a biometric monitoring device may be used to evaluate howwell the user is adhering to their desired sleep duration, as well as toprovide further tools that may assist the user in attaining theirdesired sleep duration. The sleep data may include data for a pluralityof sleep sessions and may specify various sleep states of the user forthe respective sleep sessions, as well as bedtimes, and/or waketimes ofthe user for each respective sleep session. As stated above, the varioussleep states of the user may also include or be representative of one ormore various sleep stages of the user.

Sleep monitoring and/or tracking may also include storing the sleep datain a sleep log data store as one or more sleep logs associated with anaccount assigned to or associated with the user as well as sleep logsthat include sleep data of other users of other biometric monitoringdevices. The sleep log data store may be located in one or more memoriesof one or more databases that may be communicatively connected with eachother as well as with the biometric monitoring device of the user andwith biometric monitoring devices of the other users, whether directlyor indirectly. For example, a biometric monitoring device may include aBluetooth communications interface that may allow the biometricmonitoring device to connect with a user's smartphone, which may, inturn, have a network communications interface that allows the smartphoneto wirelessly connect with a remote server or servers, such as acloud-based data storage system that may store the sleep log data store.The smartphone may thus act to relay information from the biometricmonitoring device to the sleep log data store. FIG. 6 depicts an examplesystem for implementing such sleep monitoring and/or tracking. As shown,the example system 600 includes a memory 602 and a processor 604 thatare communicatively connected with each other, as indicated by line 606.

In some embodiments, the system may include a biometric monitoringdevice 608 that includes one or more sensors 610 and a communicationsinterface 612 to communicate with the one or more processors 604 asindicated by line 614. The communications interface may use any knownhard-wired, wireless, or other communication technique. The one or moresensors 610 may be configured to generate biometric sensor data that maybe used to generate sleep data. In some embodiments, the sensor 610 ofthe biometric monitoring device 608 may generate sensor data and thememory 602 may contain instructions for controlling the processor 604 toobtain sleep data that is derived from the sensor data. In some suchembodiments, the processor 604 may be part of the biometric monitoringdevice 608, on the user's computing device (e.g., a computer or a smartphone), and/or on one or more computing devices that are separate fromthe biometric monitoring device 608.

It is to be understood that, in actual practice, the processing andstorage functionality discussed herein may be distributed among a numberof different devices, rather than a single processor 604 and a singlememory 602. For example, a biometric monitoring device may have one ormore processors and a memory that are communicatively connected with oneanother and with one or more sensors of the biometric monitoring device.The memory of the biometric monitoring device may storecomputer-executable instructions for controlling the one or moreprocessors of the biometric monitoring device to obtain sensor data fromthe one or more sensors. In some implementations, the memory of thebiometric monitoring device may also store computer-executableinstructions for controlling the one or more processors of the biometricmonitoring device to analyze such collected sensor data to determine oneor more biometric measurements, e.g., heart rate, steps taken, sleepstate or stage, etc., as well as to communicate collected sensor dataand/or determined biometric measurement data to a remote device, such asto a smartphone that may be communicatively connected with the biometricmonitoring device (e.g., via Bluetooth connection) and/or a server, suchas a cloud-based server. The remote device, which may have its own oneor more processors and one or more memories, may store data provided bythe biometric monitoring device in the one or more memories of theremote device, and may, in some implementations, perform furtheranalysis on such data in order to determine additional biometricmeasurements or to refine previously-determined biometric measurements.For example, a biometric monitoring device may determine some biometricmeasurements using simplified computational methods that, while lessaccurate, may consume far less battery power than more involvedcomputational methods. If the sensor data used to determine suchbiometric measurements is later transmitted to a remote device, e.g., toa server, the sensor data may be re-analyzed by the one or moreprocessors of the remote device using more computationally-intensivetechniques that provide a more accurate determination of that samebiometric measurement. Thus, it is to be understood that thedetermination and storage of sleep data may be performed in any of anumber of different devices and/or locations, and the techniquesdiscussed herein are not to be viewed as being limited to implementationon only one type of device or system.

As discussed above, sleep data may be stored in a sleep log data store,which may be a data structure that stores sleep data and associates suchsleep data with particular sleep sessions. There may be multipleinstances of a sleep log data store. For example, a biometric monitoringdevice may store several days' worth of sleep sessions and sleep data,and a smartphone that is communicatively connected with the biometricmonitoring device may store several weeks' or months' worth of sleep logdata, and a remote device, e.g., a cloud-based storage system, may storemonths or years of sleep log data (or even store such dataindefinitely).

It is also to be understood that the techniques discussed herein mayinvolve calculations and/or determinations that may be made byprocessors located in a variety of different locations. For example, insome implementations, a target bedtime determination (discussed laterherein) may be made by a processor or processors of a wearable biometricmonitoring device using data stored on the biometric monitoring device.In other implementations, such a target bedtime determination may bemade by a processor or processors of a mobile communications device,e.g., a smartphone, that is paired with the wearable biometricmonitoring device. In yet other implementations, such a target bedtimedetermination may be made by a processor or processors of a remotedevice, such as a cloud server system, and the target bedtimedetermination (or a recommended bedtime determination based thereupon,also discussed later herein) may be sent to a mobile communicationsdevice, e.g., a smartphone, afterwards to allow such information to beprovided to the user via a GUI of the mobile communications device.

New User/User New to Sleep Tracking—Initial Selected Sleep DurationSetup

As discussed earlier, in some implementations, a selected sleep durationmay be obtained for a new user. Various further details of how suchselected sleep durations may be obtained are discussed in more detailbelow, along with graphical user interfaces that may be used to assistin obtaining such selected sleep durations.

FIGS. 7 and 8 depict examples of two different graphical user interfacesthat may be generated for a new user (each represents a different optionfor introducing the new user to sleep tracking). As can be seen, suchgraphical user interfaces (referred hereinafter as a “GUI” or “GUIs”)may include placeholders 701 for information such as historical sleepdata of the user, as well as a welcome message—such historical sleepdata may be collected by the biometric monitoring device even if theuser has not previously used or otherwise enabled the sleep trackingfunctionality of the biometric monitoring device before, although inthis case, there is no historical sleep data, so “no data” is displayed(in the GUI of FIG. 8) and there are no indications of actual hoursslept. These GUIs may also include a “Get Started” prompt which mayobtain input from the user to setup sleep tracking for the user. As usedherein, a “prompt” may receive information and/or input from a user.Further, also as used herein, any time a user is described as inputtingany data or information into a GUI, including selecting a prompt in aGUI, it may be considered that a processor providing such a GUI orotherwise linked or associated with the GUI, e.g., a processor at aremote server that is provided such data or information by, for example,accessing such data from a memory where it has been stored, obtains suchdata, information, and/or selection. Such setup for sleep tracking mayinclude the generation of additional GUIs to obtain sleep-relatedinformation from the user and to display sleep-related information, aswill be described in more detail below. FIG. 9 depicts another GUI thatmay be generated for a new user or a user being introduced to sleeptracking. If the user responds to the prompt to “Get Started” with sleeptracking, the new user may, in some implementations, be introduced tothe concept and importance of sleep tracking, e.g., by one or morefollow-on GUIs such as the GUI of FIG. 9. The GUI of FIG. 9 may includesleep-related information, such as information regarding the importanceof sleep, as well as a prompt, e.g., “OK”, which may be selected inorder to transition to another GUI, such as the GUI of FIG. 10. The GUIof FIG. 9 may be generated before or after FIGS. 7 and 8 or may, in somecases, be omitted entirely.

FIG. 10 depicts a GUI that may be generated as part of a technique forobtaining a selected sleep duration of the user. As used herein,“selected sleep duration” is used synonymously with “sleep goal,” andrepresents the amount of actual sleep (as may be estimated by abiometric monitoring device) that the user would like to achieve duringa sleep session or sleep sessions. As can be seen, the GUI of FIG. 10includes a prompt 1002 that can be user-adjusted to specify an amount ofsleep 1001 that the user believes he or she gets during a typical sleepsession, such as typical night's sleep. In some implementations, theamount of sleep entered into the prompt may be used as a starting pointfor obtaining a selected sleep duration via additional GUIs, as isdiscussed further below. In other implementations, the amount of sleepinput by the user to the GUI, i.e., obtained by the GUI of FIG. 10, maybe considered the selected sleep duration (or sleep goal) of the userwhich may be used in other aspects of sleep tracking as discussed below.In this later implementation, the call of the GUI shown in FIG. 10 mayspecify “Please set an amount of sleep you would like to get” or somevariant thereof.

In some implementations, another GUI may be generated after the amountof sleep is input to the GUI of FIG. 10. For instance, FIG. 11 depicts agraphical user interface that may be generated after FIG. 10. As can beseen, the GUI of FIG. 11 may include, e.g., display, the amount of sleep1101 obtained by the GUI of FIG. 10 and may provide a prompt 1102 toallow the user to confirm that the typical amount of sleep input by theuser is enough sleep or to indicate that the user would like to get moresleep. The GUI may obtain inputs from the user when the user selects the“Yes” or “No, I Want More” prompts (or similar prompts) which may beincluded in the GUI.

In some such implementations, if the user inputs in a GUI, such as theGUI of FIG. 11, that the amount of sleep is sufficient, then another GUImay be generated, such as FIG. 12, which depicts a GUI that may begenerated after FIG. 11. Here, as with the GUI of FIG. 11, the GUI ofFIG. 12 may include the amount of sleep 1201 obtained in FIG. 10 as wellas information related to the amount of sleep obtained in FIG. 10. Forexample, if the amount of sleep obtained in FIG. 10 is outside a rangeof predetermined sleep durations, then information may be included inthe GUI that is intended to educate the user about recommended sleepand/or encourage the user to change the selected sleep duration to avalue within the range of predetermined sleep durations. For instance,in FIG. 12, the information included in the GUI informs the user thatthe selected sleep duration of 6 hours and 30 minutes is outside arecommended sleep duration range of 7-9 hours for adults. However, theGUI of FIG. 12 also acknowledges that every person is different, therebygently educating the user as to the recommended sleep duration.

If the user inputs that more sleep is desired in the prompt 1202 of theGUI of FIG. 10, then the GUI of FIG. 13 may be generated. The GUI ofFIG. 13 includes sleep-related information that may be displayed to theuser, similar to that displayed in FIG. 12, which here is informationthat informs the user about recommended sleep durations for adults;since the user has already indicated dissatisfaction with their typicalsleep duration (by way of selecting the “No, I Want More” prompt), theinformation regarding the typical sleep durations of adults may bepresented in a more emphatic manner than in the GUI of FIG. 12, e.g.,the phrase “but everyone is different” may be omitted. The GUI of FIG.13 also includes a recommended sleep duration 1301 which may bedetermined in any number of various ways, such as are described indetail below. In some embodiments, the recommended sleep duration may becalculated based, at least in part, on past or historical user-selectedsleep duration; sleep data, including the selected sleep duration, ofother users of sleep tracking and/or biometric monitoring devices; alook-up table; demographics of the user (e.g., age, weight, gender); ageographic location of the user; specific days of the week; a specifictime of year; holidays; a seasonal time change; exercise of the user;duration of daylight; and/or general guidelines of sleep durations forusers. For example, in FIG. 13, the system may set the recommended sleepduration to 7 hours and 00 minutes, which is the lowest sleep durationin the depicted range of recommended sleep durations for adults. Suchrecommended sleep duration may also be calculated by adding anincremental amount to the selected sleep duration specified in theprompt 1002 in FIG. 10, such as increment the selected sleep duration by15 minutes or 30 minutes. The user may then indicate that they find theselected sleep duration acceptable or unacceptable via a prompt 1302.

The GUIs of both FIGS. 12 and 13 also include prompts which may obtainthe user's selection of whether the recommended or selected sleepduration that is depicted is acceptable (“Sounds Good!”) or whether theuser would like to choose her own sleep duration (“Choose My Own”). Ifthe user selects the “Sounds Good!” prompt, then the recommended sleepduration as shown on either of the GUIs shown in FIG. 12 or 13 may bestored as the selected sleep duration.

If a GUI obtains the user's input to choose her own sleep duration,e.g., the user selects the “choose my own” prompt in either of the GUIsof FIG. 12 or 13, then another GUI may be generated. FIG. 14 depicts agraphical user interface that may be generated after 13 and, as can beseen, this GUI includes a selected sleep duration 1401 and a prompt 1402to allow the user to change the selected sleep duration. This selectedsleep duration may, like in FIG. 13, override any previously obtainedselected sleep durations and become the selected sleep duration.

Once the selected sleep duration is obtained by the GUI, a memorycommunicatively connected with one or more processors may includeinstructions for controlling the one or more processors to store theselected sleep duration in the memory or in a sleep log data store suchthat the selected sleep duration is associated with an account assignedto or associated with the user, as discussed further below. The sleeplog data store may, for example and as discussed earlier, be a databaseor other structured data construct that stores sleep logs associatedwith one or more user accounts (which are each associated with aparticular user). A sleep log, for example, may be a record that isrepresentative of sleep data from a user's sleep session, e.g., it mayhave data indicating the start and end of the sleep session, the amountsof time spent in various sleep states, the number of times the personwoke up during the sleep session, etc. There may be multiple instancesof sleep log data stores that may be utilized in the variousimplementations discussed herein—for example, a local sleep log datastore may be maintained in the memory of a user's biometric monitoringdevice or smartphone and may be used to store sleep logs for that user.Alternatively, or additionally, an external server or servers, e.g., acloud-based system, may provide a global sleep log data store that maybe used to store sleep logs for that user as well as multiple otherusers; processors having access to either type of sleep log data storemay be able to access sleep data from the sleep logs stored therein andto perform calculations utilizing such data.

Instructing Users on how to Enable Sleep Tracking in a BiometricMonitoring Device

After the user's selected sleep duration is obtained, in someimplementations a GUI may be generated that includes information relatedto the user's biometric monitoring device, such as further instructionsfor how to use their biometric monitoring device to track sleep. In someembodiments, a determination may be made as to whether the biometricmonitoring device of the user is a biometric monitoring device withautomatic sleep tracking functionality or a biometric monitoring devicewith manually-triggered sleep tracking functionality. This may bedetermined, for example, by comparing the model of the biometricmonitoring device against a list or lists of biometric monitoringdevices having automatic sleep tracking or manual sleep tracking. Forsome biometric monitoring devices that may not be configured toautomatically detect, track, and/or monitor the user's sleep, the GUI ofFIG. 15 may be generated. FIG. 15 depicts a graphical user interfacegenerated for biometric monitoring devices that do not include automaticsleep detection, tracking, and/or monitoring. As can be seen, the GUI ofFIG. 15 includes information confirming that the selected sleepduration, i.e., sleep goal, has been set. As noted above, once theselected sleep duration is input, it may be stored in a memory and/orsleep log data store such that it is associated with the user. The GUIof FIG. 15 also includes instructional information about using thebiometric monitoring device in order to monitor and track the user'ssleep—in this case, the user is instructed to push and hold a button onthe biometric monitoring device to mark the start and stop sleeptracking, e.g., the beginning and end of a sleep session. For somebiometric monitoring devices that may be configured to automaticallydetect, track, and/or monitor the user's sleep, the GUI of FIG. 16 maybe generated. Here, the GUI of FIG. 16 includes information confirmingthat the selected sleep duration, i.e., sleep goal, has been set, aswell as instructional information about using the biometric monitoringdevice in order to monitor and track the user's sleep—in this case, theuser need only wear the biometric monitoring device and the biometricmonitoring device may automatically identify when the user is in a sleepsession, so the user is only prompted to remember to wear the biometricmonitoring device to bed. It will be appreciated that the particular GUIdisplayed, e.g., such as the GUIs of FIGS. 15 and 16, may be selectedbased on the determination as to whether or not the user's biometricmonitoring device has automatic sleep tracking.

FIG. 17 depicts a graphical user interface that may be generated afterFIG. 15 or 16. Here, the GUI may include information about makingchanges to the selected sleep duration, i.e., the sleep goal, as well asinformation related to sleep data of the user, such as sleep durationdata, waketimes, and/or bedtimes. For example, the “You can edit yoursleep goal here” callout may be used to indicate a user interfaceelement that may allow a user to access, for example, a dashboard GUIsuch as is shown in FIG. 38, which is discussed later, which may allowthe user to easily change various sleep scheduling parameters. For a newuser, the GUI of FIG. 17 may include the capability to display sleepdata, but there may not yet be enough (or any) sleep data generatedand/or collected by the user's biometric monitoring device to display inthe GUI of FIG. 17.

Establishing a Sleep Schedule—Potential Recalibration of Selected SleepDuration

FIGS. 18 and 19 depict graphical user interfaces that may be generatedafter some sleep data derived from sensor data generated by thebiometric monitoring device have been obtained and stored in the sleeplog data store. Such sleep data may indicate that the user isconsistently unable to meet the selected sleep duration, and may thedisplay of a GUI that allows for recalibration of the selected sleepduration to a selected sleep duration that is perhaps easier for theuser to achieve. Such interfaces, or similar interfaces, may also beused during initial sleep duration setting if sleep data has alreadybeen obtained at the time that the initial selected sleep duration isset.

The GUIs in FIGS. 18 and 19 include the graphical representation of atleast some sleep data of the user relating to actual time slept. Forinstance, each of the two GUIs include a graphical representation 1801or 1901 of the “Hours Slept” of the user for particular days of a weekas well as an average amount of the “Hours Slept” for the week. The“Hours Slept” information may be derived from the obtained and storedsleep data, and may be representative of various sleep states of theuser, depending on what sleep states are considered to be “sleeping.” Asalso seen in these Figures, the GUIs may include a prompt for the userto set a sleep schedule.

In some implementations of generating one or more personalized GUIs forsetting the sleep schedule of the user of the biometric monitoringdevice, a GUI may be generated that is similar to the GUIs of FIGS. 11through 13, except that instead of a user-supplied typical sleepduration, the GUI may provide a calculated typical sleep duration basedon the actual sleep durations determined for one or more sleep sessionsfor which biometric sensor data has been obtained. FIGS. 20 and 21, forexample, depict graphical user interfaces that may be used, at least inpart, to obtain a selected sleep duration of the user after sleep datafor some period of time has been obtained. As can be seen in FIGS. 20and 21, the GUIs include information 2001 or 2101 related to and/orbased, at least in part, on the sleep data of the user that has beencollected. Such information may include sleep duration, e.g., averagetime spent in the various sleep states during some number of past sleepsessions and/or during a specific period of time, e.g., the past week orpast two weeks. Here, the GUI of FIG. 20 includes an average amount ofsleep for the user for the last five sleep sessions (i.e., “sleeps”),whereas the GUI of FIG. 21 includes an average amount of sleep for theuser during the previous 14 day period. These GUIs may also includeprompts 2002 or 2102 which may receive input from the user to eitheraccept the presented sleep duration (“Yes”) as the selected sleepduration, i.e., sleep goal, or to select a different selected sleepduration (“No, I Want More”).

FIG. 22 depicts a graphical user interface that may be generated toobtain an updated selected sleep duration. The GUI of FIG. 22 may begenerated after FIG. 20 or 21 if the user input that the informationdisplayed in the GUI of FIG. 20 or 21, e.g., average sleep duration ofthe user, is acceptable as the selected sleep duration. The GUI of FIG.22 may also include a recommended sleep duration 2201 that is related tothe information presented in the GUI of FIG. 20 or 21, such as a sleepduration within a particular amount of time to the information presentedin the GUI of FIG. 20 or 21. For example, FIGS. 20 and 21 includedactual measured average sleep durations and the GUI of FIG. 22 includesa recommended sleep duration that is not the same as these average sleepdurations, but is within three minutes of these average sleep durations.In some embodiments, the recommended sleep duration may, for example, berounded to the closest 15-minute increment to the average sleep durationpresented in the GUI of FIGS. 20 and 21. In some other embodiments, therecommended sleep duration of the GUI in FIG. 22 may be the same as thesleep duration presented in FIG. 20 or 21.

Prompts 2202 are included in FIG. 22 for the user to either accept therecommended sleep duration as the selected sleep duration, i.e., sleepgoal, or to choose a different selected sleep duration. If the useraccepts the recommended sleep duration, then the recommended sleepduration is stored as the selected sleep duration of the user. If theuser elects to choose a different selected sleep duration, then FIG. 23may be generated. FIG. 23 depicts a graphical user interface that may begenerated to, at least in part, obtain a user-specified selected sleepduration. FIG. 23 includes prompts to obtain an input sleep duration bythe user which may be stored as the selected sleep duration.

FIG. 24 depicts a different graphical user interface that may begenerated to obtain a selected sleep duration, for example, in responseto the user selecting “no, I want more” in the GUI of FIG. 21. Here, theGUI may include a recommended sleep duration 2401 that is more than thesleep duration information included in the GUI of FIG. 20 or 21. Asstated above, the GUIs of FIGS. 20 and 21 included average sleepdurations of the user and here in FIG. 24, the recommended sleepduration is greater than those average sleep durations. The amount bywhich the recommended sleep duration is greater than the presented sleepduration may vary. In some implementations the amount may be 30 minutesfrom the sleep duration information previously presented to the user;for example, if the sleep duration information presented to the user, asin FIG. 21, is an average sleep duration of 6 hours and 48 minutes, thenthe recommended sleep duration may be 7 hours and 18 minutes. In someimplementations, the amount may be 30 minutes from the nearest 15-minuteincrement of the presented sleep duration information; for instance, ifthe sleep duration information presented to the user, as in FIG. 21, isan average sleep duration of 6 hours and 48 minutes, then the closest 15minute increment to that sleep duration is 6 hours and 45 minutes andtherefore the recommended sleep duration may be 7 hours and 15 minutes,as depicted in FIG. 24.

Similar to FIG. 22, FIG. 24 includes prompts 2402 for the user to eitheraccept the recommended sleep duration as the selected sleep duration orto choose a different selected sleep duration. If the user accepts therecommended sleep duration, then the recommended sleep duration may bestored as the selected sleep duration of the user. If the user elects tochoose a different selected sleep duration, then FIG. 23 may begenerated which includes prompts 2302 to obtain an input sleep duration2301 by the user which may be stored as the selected sleep duration.

FIG. 25 depicts a graphical user interface that may be generated after aselected sleep duration has been selected by the user. This GUI may begenerated after, for instance, the selection of the selected sleepduration in FIGS. 22 through 23. This GUI may serve as an introductionto setting up a sleep schedule.

Establishing a Sleep Schedule—Establishing a Scheduled Waketime

In some implementations of generating one or more personalized GUIs forsetting the sleep schedule of the user of the biometric monitoringdevice, the generation may include generating one or more GUIs to obtainthe scheduled waketime of the user. FIG. 26 depicts a graphical userinterface that may be generated, at least in part, to obtain thescheduled waketime of the user. The GUI of FIG. 26 may includeinformation 2601 related to and/or based, at least in part, on the sleepdata of the user, which may include past waketimes of the user. Suchpast waketimes may be an average waketime for a specific period of time,such as two weeks; an average waketime for a specific number of sleepsessions; a single past waketime of the user; or another metric of pastwaketimes of the user. This information may, in some embodiments, beconsidered a recommended waketime. Such a past waketime may, forexample, be rounded to the nearest 15-minute interval on-the-hour tomake it easier for the user to interpret.

FIG. 26 may also include prompts 2602 for the user to accept thedisplayed information (e.g., an average waketime) as the target orscheduled waketime. In some such embodiments, as discussed above, thisinformation may be a recommended waketime which the user may accept byselecting one prompt of FIG. 26 (e.g., “Set As Target”) or may change byselecting another prompt of FIG. 26 (e.g., “Choose A Different Time”).If the user selected the recommended waketime as the scheduled waketime,then the recommended waketime is stored as the scheduled waketime. Thescheduled waketime may be used to set an alarm to wake the user up atthe scheduled waketime, but, more importantly, the scheduled waketimemay be used in determining a recommended bedtime for the user based ontheir selected sleep duration.

FIG. 27 depicts a different graphical user interface that may begenerated, at least in part, to obtain the scheduled waketime of theuser. The GUI of FIG. 27 may be generated if the user selects to choosea different waketime in the GUI of FIG. 26. Here, the user may beprovided with a prompt 2702 to allow the user to input any waketime 2701which will be received by the GUI and stored as the scheduled waketimeof the user.

Establishing a Sleep Schedule—Setting Waketime Alarms

FIG. 28 depicts a graphical user interface generated to obtain userpreferences regarding a waketime alarm. The GUI of FIG. 28 may, forexample, provide an indication 2801 of when the waketime alarm would beset for, as well as prompts 2802 to allow the user to enable or disablethe waketime alarm. The GUI of FIG. 28 may alternatively or additionallyobtain timing and/or day information regarding the waketime alarm suchas a time, a date, and/or a specific day for which the waketime is alarmis to be activated. In some embodiments, the GUI of FIG. 28, or anotherGUI, may obtain a selected notification for the scheduled waketimealarm; the user may select the mechanisms in which the waketime alarmwill occur, such as an auditory output, an electronic communication, anelectromagnetic communication, a visual output (such as very brightlight that may simulate daylight), and/or a tactile output that isdescribed in greater detail below. In some such implementations, thebiometric monitoring device may include a notification mechanism that isconfigured to provide such notifications; alternatively, such anotification may be generated by a smartphone that is paired with thebiometric monitoring device.

Establishing a Sleep Schedule—Establishing a Selected Bedtime

In some embodiments, once the user's selected sleep duration, i.e.,sleep goal, has been obtained and stored, the user's sleep may betracked for a specified or predetermined period of time and/or number ofsleep sessions before the generation of additional personalized GUIs,including a GUI based on the calculation of a specifically tailoredtarget bedtime for the user, as is described in more detail in a latersection. These additional GUIs may be used to assist the user withmodifying their behavior so as to better achieve their selected sleepduration. As most people have little flexibility in the time they wakeup, e.g., they must get up at a certain time in order to arrive at workor school at a scheduled time, such GUIs may generally be designed topromote the selection of a recommended bedtime that will allow the userto reliably achieve their desired sleep duration—a user typically hasmuch more control over their bedtime, so helping the user establish andadhere to an appropriate bedtime may be the most effective technique forpromoting regular sleep patterns.

In some implementations, a recommended bedtime may only be providedafter a minimum amount of sleep data for the user is acquired. Forinstance, the minimum amount of sleep data may be sleep data gatheredover a certain period of weeks, such as three weeks; it may be sleepdata gathered for a certain number of sleep sessions, such as five; orit may be a combination in which the minimum is sleep data gathered fora certain number of sleep sessions over a certain period of time, suchas five sleep sessions over two weeks. In some embodiments, once theminimum amount of sleep data is obtained, additional personalized GUIsmay be generated based on at least that minimum amount of sleep data. Inother implementations, the collection of sleep data of the user beforegenerating the additional personalized GUIs for recommending a bedtimemay be skipped, and the recommended bedtime may be determined accordingto other techniques, as discussed in more detail later.

In some implementations of generating one or more personalized GUIs forsetting the sleep schedule of the user of the biometric monitoringdevice, the generation may include calculating a target bedtime based,at least in part, on the scheduled waketime and a sleep efficiency basedon the sleep data for one or more users stored in the sleep log datastore. The target bedtime may be calculated in order to provide the userwith a recommended bedtime that increases the likelihood of consistentand healthy sleep. The target bedtime may also be calculated in order toprovide the user with the recommended bedtime that will most likelyresult in the selected sleep duration, e.g., sleep goal, based on thescheduled waketime.

Sleep efficiency, as used herein, refers to the total time in a sleepsession that an individual is in one or more sleep states that is or arenot an awake state, divided by the sleep session duration, or asimilarly equivalent measure of sleep efficiency. For example, theindividual may get into bed at 11:00 p.m., take 30 minutes to fallasleep, wake up periodically during the night for a total of 30 minutes,and then wake up at 7:00 a.m. Here, the individual was attempting tosleep for a total of 8 hours (11:00 p.m.-7:00 a.m.), but was actually inthe non-awake sleep state for 7 hours (8 hours in bed less 30 minutes tofall asleep and less 30 minutes in an awake sleep state during thenight). The individual's sleep efficiency for this example night is 7hours divided by 8 hours, or 0.875. Each user will have a differentaverage sleep efficiency based on their physiology, sleep environment,etc. Sleep efficiency may also change night-to-night due to variousfactors such as whether or not the user consumed alcohol and/orcaffeine, was traveling, is sick, etc. Sleep efficiency may also changedepending on when a person goes to bed—for example, if a person goes tobed too early, they will likely be less tired and it will be harder forthem to fall asleep quickly. If the person goes to bed later, they willbe more tired and spend less time falling asleep, thereby likelyincreasing the amount of time that is spent sleeping in that sleepsession.

It is to be understood that sleep efficiency may be evaluated based onhow much total time a person spends in one or more designated sleepstates during a sleep session divided by the sleep session duration. Insome implementations, the designated sleep states may include allnon-awake sleep states, in which case the sleep efficiency may be thetotal time in a sleep session spent asleep (regardless of how restlessthe person is while sleeping) divided by the sleep session duration.This is perhaps the simplest type of sleep efficiency to determine. Inother implementations, however, sleep efficiency may be more nuanced,e.g., the designated sleep states may only include a non-empty subset ofthe non-awake sleep states, such as the sleep states that are known tobe most restful and recuperative for the human body. In such animplementation, the sleep efficiency may be much lower than in thetotal-time-asleep example discussed previously. The selection of whichsleep states to include in the determination of sleep efficiency may bepre-set or may be selectable by the user.

Because sleep efficiency is typically less than 1 (while it is nevermore than 1, it can, theoretically, be equal to 1 if a personimmediately falls asleep when going to bed and otherwise spends theentire sleep session in the desired sleep states), recommending abedtime by simply subtracting the selected sleep duration from thescheduled waketime does not result in an effective recommended bedtime.For example, if the user wants to wake up at 7:00 a.m. and get 8 hoursof sleep, then subtracting 8 hours from 7:00 a.m. to reach a recommendedbedtime of 11:00 p.m. (which is exactly 8 hours before 7:00 a.m.), willgenerally result in the user not receiving the full 8 hours in thevarious desired non-awake sleep states. As discussed herein, the sleepefficiency and various other factors may be used to calculate a moreaccurate, personalized, recommended bedtime for the user that will tendto cause the user to achieve the selected sleep duration.

As stated above, the system depicted in FIG. 6 may be used to implementsuch calculations of the target bedtime. Similar to above, the memory,which is communicatively connected with the one or more processors, maystore instructions for controlling the one or more processors to obtainsleep data derived from sensor data generated by the biometricmonitoring device, the sleep data including a plurality of sleepsessions that each specify various sleep states of the user for therespective sleep sessions, and to store the sleep data in the sleep logdata store as one or more sleep logs associated with an account assignedto the user. In some implementations, the sleep log data store may alsoinclude sleep logs including sleep data derived from sensor datagenerated by other biometric monitoring devices of other users. Thememory may also store instructions to calculate the target bedtimebased, at least in part, on the scheduled waketime of the user and thesleep efficiency that is based on the sleep data for one or more usersstored in the sleep log data store.

In some implementations, the recommended bedtime may be based, at leastin part, on the sleep efficiency of the user of the biometric monitoringdevice. In some other implementations, the recommended bedtime may bebased, at least in part, on the sleep efficiency of other users of otherbiometric monitoring devices; these other users may be filtered andselected in any number of various ways, which is discussed below.

The sleep efficiency may thus be representative, at least in part, of acorrelation between sleep state duration data for one or more sleepsessions and sleep session duration data for those respective one ormore sleep sessions. The memory may store instructions for controllingthe one or more processors to obtain sleep state duration data for asleep session stored in the sleep log data store and to determine sleepsession duration data for a sleep session stored in the sleep log datastore; the sleep state duration data and sleep session duration data mayboth be based, at least in part, on the sleep data derived from thesensor data generated by the biometric monitoring device. The sleepstate duration data, as mentioned above, may be representative of thetotal amount of time the user that is associated with the sleep sessionspent in a designated subset of sleep states during the sleep session.The subset of sleep states may include non-awake sleep states, but doesnot include awake sleep states. While most sleep data may be obtaineddirectly from sensor measurements, some sleep data, e.g., the start andend of a sleep session, may potentially be obtained from user input,e.g., when a user pushes a button on a biometric monitoring device tomark the start and/or end of a sleep session.

The sleep efficiency that is calculated for a person may be used tobetter predict how long of a sleep session that person will require inorder to achieve the selected sleep duration for that person, which may,in turn, drive the determination of a target bedtime for that person.For example, the sleep efficiency may, in some embodiments, be modeledas a constant, e.g., an average, value based on one or more sleepsessions and sleep session duration data for those respective one ormore sleep sessions. For instance, a user may have a sleep efficiency of0.94 which may be an average sleep efficiency over a specific period,such as the last 7 days, one month, or longer. If the user typicallydesires 8 hours of total sleep duration per sleep session, i.e., theuser's selected sleep duration is 8 hours, then the sleep sessionduration that the user will likely need in order to attain 8 hours oftotal sleep may be estimated by dividing the selected sleep duration bythe sleep efficiency, e.g., 8 hours divided by 0.94, which results in atarget sleep session duration of approximately 8 hours and 30 minutes.This may also be thought of as multiplying the selected sleep durationby a factor that is based on the sleep efficiency, e.g., a factor suchas 1/sleep efficiency. This target sleep session duration, in turn, maybe subtracted from the scheduled waketime in order to determine a targetbedtime, e.g., if the user's scheduled waketime is 7:00 a.m., then toreach 8 hours of total sleep duration in the designated sleep states,the target sleep duration of 8.5 hours may be subtracted from thescheduled waketime to yield a target bedtime of 10:30 PM the nightbefore.

Sleep efficiency may be accounted for in determining a target bedtime ina variety of different ways. In some target bedtime determinationtechniques, sleep efficiency may be determined directly, as in the aboveexample, and then applied to a selected sleep duration to arrive at atarget sleep session duration which may then be subtracted from ascheduled waketime to determine the target bedtime. In other techniques,the sleep efficiency may be inherently accounted for in the targetbedtime without ever directly calculating it—in either case, however,sleep efficiency is accounted for in the target bedtime. An example ofsuch inherent accounting for of sleep efficiency is discussed below.

FIG. 29 depicts a graph of a linear relationship between sleep stateduration data for one or more sleep sessions and sleep session bedtimedata for those respective one or more sleep sessions. Such data for aplurality of sleep sessions with generally consistent waketimes (e.g.,approximately 7:30 AM) are plotted in the graph with sleep stateduration in hours on the x-axis and bedtime on the y-axis; the linethrough the data points represents a linear relationship, e.g., asdetermined by applying a linear regression model to the data, betweenthese two data points that inherently accounts for sleep efficiency.Thus, for example, if the scheduled waketime is 7:30 AM, then theregression model that is applied to the data of FIG. 29 may be used todetermine the target bedtime as a function of the selected sleepduration. In this example, if the selected sleep duration is 8 hours,then the target bedtime that may be calculated based on the regressionmodel may be 10:22 PM, which represents a target sleep session durationof 9 hours and 8 minutes; the sleep efficiency for such a sleep sessionwould be 8 hours divided by 9.14 hours, or 87.5%. Similarly, if theselected sleep duration is 7 hours, then the target bedtime that may becalculated based on the linear regression model in this example is 11:30PM, which represents a target sleep session duration of 8 hours; thesleep efficiency for such a sleep session would also be 87.5% since thesleep efficiency that is inherently reflected by the linear regressionmodel is a constant sleep efficiency. This constant sleep efficiency issimilarly observable if one calculates a target bedtime using the linearregression model for a selected sleep duration of 6 hours—the targetbedtime for such a sleep duration would be 12:39 AM and the sleepsession duration would be 6 hours 51 minutes, which, again, reflects an87.5% sleep efficiency. Since the sleep data store may include the starttime (the bedtime) and the end time (the waketime) for each sleepsession in a plurality of historical sleep sessions, applying a linearregression model (or other data-fitting model) to such data may allowfor direct determination of a target bedtime that accounts for sleepefficiency without requiring the separate determination of the actualsleep efficiency.

Sleep efficiency may also be accounted for in more sophisticated ways toallow for more refined determinations of target bedtime that are evenmore customized or personalized to the sleep habits of the user. In theexample above, the linear relationship between bedtime and sleepduration assumes that sleep efficiency is individually uncorrelated tobedtime, waketime, and/or sleep state duration, e.g., constant, and formany users this assumption may not hold true in practice. For example, auser that wakes up at the same time every day (e.g., 7:00 a.m.) and goesto bed at different times may consequently spend different amounts oftime in non-awake sleep states for each sleep session, e.g., each night.For sleep sessions where the user goes to bed late, the user may be moretired than usual, may fall asleep faster than normal, and therefore mayhave more efficient sleep. Conversely, if the user goes to sleep earlierthan normal, they may not be as tired and it may take them more time tofall asleep, thereby lowering their sleep efficiency. This may create anon-linearity in the relationship between sleep state duration data forone or more sleep sessions and sleep session duration data for thecorresponding one or more sleep sessions because the user's sleepefficiency is correlated with bedtime such that, for instance, laterbedtimes may result in increasingly efficient sleep and thus more sleepthan a linear relationship may predict.

Therefore, as discussed herein below, in some embodiments thecalculation of the target bedtime may account for a variable sleepefficiency that may be affected by one or more factors and thus be moreaccurate than a sleep efficiency that is assumed to be constant.

In regression model-based approaches to determining target bedtime, theregression model may be any type of regression model including, but notlimited to, a linear regression model, a nonlinear regression model, aparametric regression model, a nonparametric regression model, asemiparametric regression model, and a multivariate linear regressionmodel. For instance, a parametric model (e.g., a polynomial) may be fitto sleep data including sleep state duration data, waketimes, sleepsession duration data, and/or bedtimes to account for non-linearrelationships between the variables. For instance, the recommendedbedtime may be provided using a function modeled on:

target bedtime=Σ_(n=0) ^(N)∝_(n)(sleep state duration data)^(n)

in which ∝_(n) are the weights for each polynomial term (up to order N)that could be determined via regression fitting. For example, in thecontext of a linear regression, an example of such a function may haveonly two terms, the coefficients of which (∝₀ and ∝₁) are determined vialinear regression analysis of sleep data for one or more users, e.g., ofthe user for which the target bedtime is being determined, and which mayinherently account for the effects of sleep efficiency on the targetbedtime for a given selected sleep duration since the linear regressionmodel (or whatever regression model is used) will have been “trained” orcalibrated using sleep data that is reflective of such sleep efficiency.In such an example, ∝₀ may represent the individual's average waketimeless the average amount of time spent in a non-efficient sleep stateduring an average sleep session, and ∝₁ may be another coefficient thatencodes sleep efficiency with respect to sleep duration—if sleepefficiency changes linearly with sleep state duration, then ∝₁ would beabove or below −1, but not exactly −1, such that:

target bedtime = ∝₀+∝₁(selected sleep duration) = average waketime taking into account sleep efficiency − selected sleep duration taking into account sleep efficiency

The training of the regression model may be further enhanced, forexample, by using a subset of training data (sleep data) that is withina certain threshold range of the scheduled waketime, if desired. FIG. 30depicts a parametric regression model fit to the data of FIG. 29. As canbe seen, the curve in FIG. 30 conforms more closely to the data than inFIG. 29, and may therefore more accurately account for sleep efficiency.It is to be understood that the above example is merely one example ofhow sleep data may be used to train a regression model. It will beappreciated that other variations may train a regression model usingother combinations of sleep data, e.g., target bedtime may be determinedaccording to a regression model that is a function of scheduled waketimerather than selected sleep duration (using a subset of sleep data thatis inclusive of the selected sleep duration, for example), and that thecoefficients used may represent a variety of different.

Other regression model techniques may include lasso, ridge, Bayesian,and maximum likelihood methods. In some such embodiments, the regressionmodel may explicitly include waketimes and/or the measured sleepefficiency in the regression model. Collinearity in the curve fit mayalso be mitigated by any known technique including, for instance,Principal Components Analysis. Additionally, known uncertainties in thecalculations and/or estimates of bedtime and/or sleep state duration maybe included in the regression model, e.g., via maximum likelihoodfitting. In some embodiments, outliers may be accounted for in theregression model to improve the fit by, for example, using Random SampleConsensus or other known iterative fitting methods.

In some implementations, the influence of various factors, such as thosediscussed herein, may be accounted for as co-variates that are used inthe regression model. For example, if desired waketime is to beaccounted for, then the above framework may be modified to:

$\text{target bedtime} = {{\sum\limits_{n = 0}^{N}\; {\alpha_{n}{\text{(selected sleep}\left. {duration} \right)^{n}}}} + {\beta \text{(scheduled waketime}} - \text{average waketime)}}$

in which β may be a factor determined using regression analysis and thecovariate term β (scheduled waketime—average waketime) may shift thetarget bedtime by an amount dependent on the difference between theaverage waketime and the desired waketime. For example, if a linearregression model were to be applied, then the alpha and beta parametersor coefficients would be determined according using regression fitting.

Some example non-parametric methods for the regression model include,for instance, K Nearest Neighbors (KNN), Random Forest regression, andGaussian process regression. For example, in the KNN case, a user withhistorical sleep data, a scheduled waketime, and/or a selected sleepduration, the sleep session duration data and corresponding sleep stateduration data for the plurality of sleep sessions included in theregression model may be associated with, e.g., may be the nearest, Ksleep sessions with similar waketimes, e.g., waketimes that are within afirst threshold amount of the scheduled waketime. For example, the firstthreshold may be ±15 minutes of the scheduled waketime of 7:30 a.m. andthe sleep sessions include in the regression model are those sleepsessions with waketimes ranging from 7:15 a.m. to 7:45 a.m.

Similarly, the sleep session duration data and corresponding sleep stateduration data for the plurality of sleep sessions included in theregression model may be associated with, e.g., may be the nearest, Ksleep sessions with similar sleep state duration data, e.g., withselected sleep duration data that is within a second threshold amount,e.g., within ±15 minutes, of the selected sleep duration.

In some implementations, the sleep session duration data andcorresponding sleep state duration data for the plurality of sleepsessions included in the regression model may also be associated with,e.g., may be the nearest, K sleep sessions with waketimes that haveoccurred on the same day of the week as the day of the week on which thescheduled waketime occurs. For example, if the user has a scheduledwaketime that occurs on a Monday, then the data included in theregression model may be associated with sleep sessions with waketimesthat have also occurred on a Monday. The resulting bedtimerecommendation may be the average bedtime from those K nights, or somepre-chosen quantile from the distribution of bedtimes. This may allowfor variations in sleep efficiency that are day-dependent to be takeninto account—for example, a person may generally be more tired on Sundaynights due to participating in soccer games on Sunday afternoons, andmay therefore have a higher sleep efficiency during sleep sessionsstarting on Sunday evenings and ending on Monday mornings.

In some embodiments, the regression model may account for one or moreof: one or more specific days of the week, a specific time of year,holidays, workdays of the user, non-workdays of the user, a seasonaltime change, a geographic location, travel by the user between at leasttwo time zones, exercise of the user, and a duration of daylight in aday. In some instances, a particular user's sleep data may not have datapoints that allow for such factors to be taken into account, in whichcase data from other users may be analyzed and used as a stand-in fordata associated with that user.

For example, day of the week may be included in the regression modelsince many users have distinct bedtimes, waketimes, and sleep statedurations on weekdays and weekends. For instance, some users tend to goto bed later, sleep longer, and/or sleep in later on weekends ascompared to weekdays. The relationships between these variables can bedistinct on weekdays and weekends. Therefore, for instance, theregression model may be fit to sleep data associated with sleep sessionsthat have waketimes on weekdays (when calculating target bedtimes thatare for waketimes that are on weekdays), while in another instance, theregression model may be fit to sleep data associated with sleep sessionsthat have waketimes on weekends (when calculating target bedtimes thatare for waketimes on weekends).

Similarly, time of year may be an included covariate to the regressionmodel. Sleep behavior generally changes with season, and is typicallycorrelated with the amount of sunlight per day (i.e., day length), whichin turn generally has a strong effect on the circadian rhythm. Forexample, a user may sleep 15 minutes more on average during the winterthan the summer. Specific nights of data, or sleep sessions that maystart and/or end near a holiday (e.g., a user may start a sleep sessionpast midnight of a holiday) may be excluded or de-weighted to improvethe regression model. For example, sleep behavior on officiallyrecognized holidays, even if on a weekday, may look more like a weekend(e.g., with later bedtimes, longer sleep state durations, and laterwaketimes). Time changes, such as seasonal and daylight savings, mayalso be accounted for by the regression model; such time changesgenerally effect sleep behavior due to the circadian rhythm notadjusting to time changes immediately. The regression model may excludesleep data or de-weight sleep data around time change dates because thatdata may, in some instances, be less representative of the underlyingrelationship between bedtime, waketime, and sleep state duration for auser.

Activity level in the hours before bedtime may also be accounted for inthe regression model. Such activity levels may be used to predict timeto fall asleep; for example, if a user is highly active within a certaintime period before the scheduled bedtime, the user may have more troublefalling asleep and may need to try to fall asleep sooner to achieve thesleep goal. Likewise, heart rate in the hours before bedtime may also beaccounted for in the regression model and may be used to predict time tofall asleep. For example, if the user has elevated heart rate with acertain time period before the scheduled bedtime, the user may have moretrouble falling asleep and may need to try to fall asleep sooner toachieve the sleep goal. The target bedtime may, in such cases, bedetermined based on bedtimes for sleep sessions in the sleep data storethat are associated with similar elevated heart rates that occur withinthat time period prior to the sleep session bedtime. As noted above, thetarget bedtime may be based, at least in part, on the sleep efficiencyof other users of other biometric monitoring devices or the sleepefficiency of the user of the biometric monitoring device. Accordingly,the target bedtime may be based, at least in part, on sleep dataassociated with the user of the biometric monitoring device and/or sleepdata associated with other users of other biometric monitoring devices.As stated above, the sleep data associated with the other users may bestored as corresponding sleep logs in the sleep log data store; suchsleep data may also be derived from sensor data generated by the otherbiometric monitoring devices of the other users.

Generally speaking, the target bedtime (as well as recommended sleepdurations, as discussed earlier with respect to the various GUIs dealingwith establishing a selected sleep duration) for a user may bedetermined from data that is associated with that user, as such datawill best reflect that particular user's sleep behavior, efficiency, andsleep needs. However, in situations where there is insufficient data orno data associated with that user that may be used to determine a targetbedtime (or, for example, a typical sleep duration), sleep data fromother users may be used as a stand-in for data associated with thatuser.

For instance, the aforementioned regression models may be applied tosleep data associated with the user and/or sleep data associated withother users. In some such embodiments, there may not be sufficient tomodel the user's sleep data, but it may be beneficial to determine atarget bedtime for the user even when little or no sleep data has beenobtained from the user. For example, the target bedtime for the user maybe determined based, at least in part, on the application of aregression model to sleep data from other users of similar demographicsand/or living in the same geographic region.

In some implementations, the target bedtime may also be based, at leastin part, on a proper subset of one or more of one or more specific daysof the week, a specific time of year, holidays, workdays of the userand/or other users, non-workdays of the user and/or other users, aseasonal time change, a geographic location, travel by the user and/orother users between at least two time zones, exercise of the user, and aduration of daylight in a day. For example, if the user's scheduledwaketime is on a weekday, then the target bedtime may be based, at leastin part, on the user's historical sleep data associated with that sameweekday. For another example, as mentioned above, if the user'sscheduled waketime is on a weekday but that weekday is also a holiday,then the target bedtime may be based, at least in part, on the user'shistorical sleep data associated with that same holiday, a weekend day,a different holiday, and/or other users' sleep data associated with thatsame holiday. In such implementations, the target bedtime may accountfor a sleep efficiency that may vary based on such factors, e.g., thesleep efficiency may be based, at least in part, on one or more ofhistorical sleep efficiency data associated with one or more of: aproper subset of one or more specific days of the week, a specific timeof year, holidays, workdays of the user, non-workdays of the user, aseasonal time change, a geographic location, travel by the user betweenat least two time zones, exercise of the user, and a duration ofdaylight in a day. For example, if the user's scheduled waketime is on aweekday, then the sleep efficiency may be based, at least in part, onthe user's historical sleep efficiency data associated with that sameweekday.

The target bedtime discussed above may be considered an “optimalbedtime” and such optimal bedtime may be further calculated, analyzed,and/or modified before being presented as a recommended bedtime that isincluded in the GUI. In some embodiments, the recommended bedtimediscussed above is the optimal bedtime that is included in the GUI. Evenif the target bedtime is not used as the recommended bedtime, therecommended bedtime will still be based on the target bedtime in somefashion, as will be seen in the examples below.

In some embodiments, the recommended bedtime may be selected so as to beat some regular hourly division, e.g., the closest quarter hour on thehour to the target bedtime. Thus, for example, a target bedtime of 11:24PM may cause a recommended bedtime of 11:30 PM to be selected, whereas atarget bedtime of 11:21 PM may cause a recommended bedtime of 11:15 PMto be selected. This may make it easier for a user to remember therecommended bedtime.

In some other such embodiments, the recommended bedtime may be before orafter the optimal bedtime by a particular time, such as between about 1and 30 minutes. For example, 15 minutes may be subtracted from theoptimal bedtime such that the recommended bedtime is 15 minutes earlierthan the optimal bedtime, thereby causing the user to think about goingto bed earlier. This may be useful, for example, in situations where thesleep data for the user indicates that the user is consistently late by10 to 20 minutes in actually going to bed by their recommended bedtime.

In some embodiments, the recommended bedtime may be based, at least inpart, on the user's optimal weekday or optimal weekend bedtime, or ondays of the week specified by the user.

In some other embodiments, the recommended bedtime may be based, atleast in part, on the differences between average sleep state durationdata for weekdays and weekends. For example, if the user sleeps longeron weekends than weekdays, it may indicate that the user is accumulatingsleep debt during the week that making up for it on the weekend.Averaging sleep state duration data for the entire 7-day week mayprovide a more accurate representation of the user's sleep need than forjust the work week (e.g., Monday through Friday). If the user's selectedsleep duration is lower than his inferred sleep need, the recommendedbedtime may be adjusted, e.g., with time added or subtracted to thetarget bedtime, to reduce the accumulation of sleep debt.

In some embodiments, the recommended bedtime may be based, at least inpart, on how inconsistent the user's bedtime is for a certain period oftime. For example, a user with an inconsistent bedtime may be providedwith a recommended bedtime that is later than the optimal bedtime inorder to achieve the user's selected sleep duration because suchrecommended bedtime may be a more achievable bedtime for the user.

In some embodiments, the recommended bedtime may be based, at least inpart, on a forecast of the user's future bedtime which may be moreappropriate for near-future behavior. For example, for a user with along baseline of data (e.g., numerous months or years), the user's ownsleep data may be modeled for the forecast while for users withinsufficient data, sleep data of other similar users (e.g., similardemographics, activity level, and/or geographic location) may be usedfor the forecast. In some such embodiments, both the data of the userand other users may be used for the forecast. For instance, as notedabove, the user's optimal bedtime may be based, at least in part, on theseason or time-of-year. Because sleep behavior may have a seasonalcomponent that is tied to the amount of daylight, sleep state durationmay be lower in the summer compared to the winter on average, bedtimesmay be later, and the accumulation of weekday sleep debt may bedifferent. Such trends may be incorporated into the recommended bedtimeby forecasting how the user's behavior may change in the near or longfuture. For example, when calculating a recommended bedtime for a userin North America in April, the average bedtime will trend later as thesummer solstice approaches and the amount of daily sunlight increases.Therefore, the recommended bedtime may be adjusted earlier inanticipation of this trend so that the user is more likely to achievetheir selected sleep duration in the coming months as their bedtimenaturally trends a bit later, thereby making it easier for the user toachieve the recommended bedtime and increasing user satisfaction (thismay result in a shorter sleep duration than the selected sleep duration,however—this may nonetheless be desirable if it positively reinforcesthe user experience with the sleep tracking and scheduling functionalitydiscusses herein). Alternatively, the recommended bedtime may beadjusted to be earlier than it otherwise would in anticipation of theuser wanting to stay up later due to the longer days. The user maydecide to stay up an extra hour past the recommended bedtime, but if therecommended bedtime has been shifted earlier by half an hour, then theuser may only be going to bed by half an hour later with respect to thetarget bedtime than they otherwise would. Similarly, the recommendedbedtime may be shifted in the opposite directions in preparation forwinter months, e.g., a later bedtime may be provided that is morein-line with what the user is likely to do in the coming months.Recommending a bedtime that is responsive to the user's natural behavioris more likely to keep the user engaged with healthy sleep behaviorrather than suggesting a bedtime that will be difficult to achieve.

As mentioned above, season may be a factor included in modeling sleepbehavior and making bedtime recommendations. The seasonal changes arelocation dependent, largely on latitude, but may also depend on localclimatic patterns, time zones, and/or cultural customs. When retrievingsleep data of other similar users for modeling and/or forecasting,location may be used as part of the similarity measure to account forthese dependencies.

In some embodiments, long term behavior, rather than short termbehavior, may be forecast and the recommended bedtime may thereforeaverage out seasonal modulations. In some other embodiments, therecommended bedtime may be based, at least in part, on whether a timechange (e.g., daylight savings time) has recently changed or is justabout to. As mentioned above, time changes (e.g., daylight savings time)can disrupt a user's sleep behavior and bedtime. The recommended bedtimemay be shifted earlier or later to help mitigate the effects of thesechanges. In some embodiments, the recommended bedtime may be based, atleast in part, on age and/or knowledge of whether the user is working orretired. Sleep duration, sleep efficiency, bedtime, and/or amount ofdeep sleep may change with age. For example, weekday sleep durationdecreases on average with age, except after retirement age where sleepduration starts to starts to increase again. Such trends may also beaccounted for in the recommended bedtime.

In some embodiments, the optimal bedtime recommendation may be shiftedfrom the target bedtime based, at least in part, on a value that iscalculated experimentally (e.g., via A/B testing, multi-armed bandittesting, or parameter optimization experiments). For example, a cohortof users may be defined, (e.g., male runners in New York City, between20 and 25 years of age, etc.), and some of these users may be separatedinto a control group while the remaining users may be placed into atesting group. Example tests may include determining if recommending aparticular bedtime leads to more or less adoption from users in thecontrol group as compared to a variant (e.g., that bedtime minus 10minutes) that is recommended to the testing group. Accordingly, therecommended bedtime may be adjusted because a user is in a particulartest or control group. Additionally, if such experiments indicate that aparticular bedtime recommendation methodology is more likely to lead toa desired outcome (e.g., a more consistent bedtime), then therecommended bedtime for all users that are demographically similar tothe cohort users may be adjusted accordingly.

As stated above, generating the one or more personalized GUIs mayinclude generating a GUI that includes the recommended bedtime (or thetarget bedtime, if the target bedtime is used as the recommendedbedtime). As noted and described at length above, the recommendedbedtime may be based, at least in part, on the sleep data for one ormore users stored in the sleep log data store. FIG. 31 depicts agraphical user interface that includes a recommended bedtime 3102. Ascan be seen, the recommended bedtime of “11:45 pm” is included in theGUI, along with the selected sleep duration 3101 (“6 hr 45 min”). Twoprompts 3103 may also be included in the GUI of FIG. 31 which may allowthe user to confirm and store the recommended bedtime as the user'sselected bedtime or to input a different time for the selected bedtimeof the user. FIG. 32 depicts a graphical user interface to obtain adifferent recommended bedtime 3201 using prompt 3202. Similar to otherGUIs described above, the user may input a bedtime which may be receivedand stored as the selected bedtime.

Establishing a Sleep Schedule—Setting Bedtime Reminders

In some embodiments, a bedtime reminder may also be displayed and/orpresented to the user in advance of the selected bedtime order to remindthe user to go to bed. In some embodiments, the memory may storeinstructions for further controlling the one or more processors topresent to the user, via a notification mechanism, the bedtime reminder.

The bedtime reminder may be provided, at least in part, as a“notification” which may be one or more of a message, an auditoryoutput, an electronic communication, an electromagnetic communication, avisual output, and a tactile output. For instance, the mechanism throughwhich the notification may be conveyed may, generally speaking, bereferred to as a notification mechanism. Notifications may be providedthrough a variety of media, and may, in some cases, require furtheraction by an intermediate device before being perceptible by the person.For example, a biometric monitoring device may have a notificationmechanism that includes a display or lights that are configured todisplay graphics or light up in order to catch the attention of a person(the notification, in this case, may refer to a signal that is sent tothe lights or display that cause these components to light up or displaygraphics to a person; it may also refer to the light or graphics that isemitted or displayed by components receiving the signal in response tothe signal). In some examples, the biometric monitoring device may havea notification mechanism that includes a speaker or other device capableof generating auditory output that may be used to provide thenotification (the notification in this case may be a signal that is sentto a speaker or other audio device; it may also refer to the actualaudio output that is generated by the audio device in response to thesignal). In some other or additional examples, the notificationmechanism may include a wireless interface and the notification may takethe form of an electronic or electromagnetic communication, e.g., awireless signal, that is sent to another device, e.g., a wearablefitness monitoring device such as a Fitbit activity tracker or asmartphone, associated with the person (the notification in this casemay be an electromagnetic signal; it may also refer to any audio,visual, tactile, or other output generated by the receiving device inresponse to receipt of the signal). In such scenarios, the notificationmay still be generated or initiated by the notification mechanism evenif the intended recipient device of the communication fails to beactivated or otherwise fails to convey the notification to the person.The notification mechanism may be configured to generate and/or provideone or more notifications to the user, and may include one or morecomponents that may be used to generate audio, visual, tactile,electromagnetic, or other types of notifications.

In various implementations, the notification may ultimately be providedusing any of a variety of output mechanisms, i.e. notificationmechanisms. In some implementations, the notification may includenothing more than a light on the fitness monitoring device that blinksintermittently when the user is to be reminded to go to bed. In otheradditional or alternative implementations, the notification may includeother visual feedback, e.g., graphics, text on a display, etc.; audiofeedback, e.g., melodies, speech, sound effects, etc.; tactile feedback,e.g., vibration, mild electric shock, etc.; electromagnetic signals toother devices to cause those other devices to provide feedbackperceptible to the person, e.g., signals sent to smartphones, laptops,desktops, tablets, other fitness monitoring devices, etc.; and otherforms of communicating with the person.

In some embodiments, generating the one or more personalized GUIs mayfurther include generating a GUI to obtain timing information indicatingone or more selected reminder times for a bedtime reminder andgenerating, for instance via the notification mechanism, the bedtimereminder based, at least in part, on the timing information. Thereminder time may be the time at which the bedtime reminder is presentedto the user; the timing information may be information that defines whenthe bedtime reminder is to be provided relative to the selected bedtime.FIG. 33 depicts a graphical user interface that may be used, at least inpart, to obtain timing information 3301 related to a bedtime reminder.As can be seen, the GUI of FIG. 33 includes timing information 3301 thatdefines or establishes a selected reminder time for the bedtime reminderto be presented to the user, which here is 30 minutes before theselected bedtime, i.e., the recommended bedtime.

The time included in the GUI of FIG. 33 (e.g., “10:45 p.m.”) may also beconsidered a recommended reminder time for the bedtime reminder. In somesuch embodiments, generating the one or more personalized GUIs mayfurther include calculating the recommended reminder time for thebedtime reminder based, at least in part, on the recommended bedtime,and displaying the recommended reminder time as part of the GUI, like inFIG. 33. Here, the recommended reminder time is 10:45 p.m. which wascalculated by subtracting 30 minutes from the selected bedtime. In otherimplementations, the user may simply define an offset time, e.g., 30minutes before the selected bedtime.

There are also two prompts 3302 in FIG. 33 which allow the user toselect the displayed selected reminder time as the selected remindertime such that it may be obtained and stored by the GUI, or to changethe selected reminder time. FIG. 34 depicts a graphical user interfacethat may be used, at least in part, to obtain timing information relatedto a bedtime reminder. As can be seen, the user may, via prompts 3402and 3403, input timing information 3401, such as a specific time as wellas day, for the bedtime reminder to be generated, e.g., presented viathe notification mechanism, to the user. Therefore, the GUI that isgenerated may obtain both timing information indicating one or moreselected reminder times for a bedtime reminder as well as dayinformation indicating one or more selected reminder days for thebedtime reminder. In such embodiments, generating the bedtime reminder,e.g., via the notification mechanism, may be based, at least in part, onthe timing information and/or the day information.

Similarly, in some embodiments, a different GUI may be generated toobtain day information indicating one or more selected reminder days forthe bedtime reminder, and the generating of the bedtime reminder may befurther based, at least in part, on the day information. For example,FIG. 35 depicts a graphical user interface that may be used, at least inpart, to obtain day information related to a bedtime reminder. Like inFIG. 33, the GUI of FIG. 35 may obtain information 3501 relating to areminder day or reminder days on which the bedtime reminder will bedisplayed. Such information may be a specific day and/or a grouping ofdays, such as weekdays or weekends.

As noted above, the bedtime reminder may be generated, such as by thenotification mechanism, based, at least in part, on the day informationand/or the timing information. In some embodiments, the bedtime remindermay include the recommended bedtime.

It should be noted that in many embodiments, the bedtime reminder may besent before the user's recommended bedtime so that the user has adequatetime to prepare for bed. This notification, e.g., warning window, may beset in several ways. For example, the user may set how many minutesbefore the recommended bedtime, e.g., the scheduled bedtime, the userwould like to receive the bedtime reminder. The notification may also bepersonalized for each user based on how many minutes of low activity theuser typically logs before the user falls asleep. Users who tend to winddown quickly before sleep time would need a shorter window to preparefor bed compared with users who typically have long periods of lowactivity before bed. The warning window may be adjusted based on whetheror not the user has a history of reducing his activity level afterreceiving the reminder. If the user is detected to be more active thanusual (e.g., by steps, metabolic equivalent units, workouts, elevatedheart rate) in the hours before bed, the user's bedtime reminder may beadjusted earlier to recommend a longer winding down period so that theuser is able to fall asleep in time to meet his selected sleep durationand scheduled waketime, e.g., a sleep goal and a wake goal.

The bedtime reminder may also have a snooze option. If the user doesbegin to lower their activity level, the alarm will go off again. Thebedtime reminder may also use location as an input for determiningwhether or not the user is able to react to the alarm. For example, ifthe user is not at home, the alarm may be disabled to avoid discouragingthe user who is in a situation out of their control (e.g., still atwork, or at a bar).

The bedtime reminder may also function as a feedback loop or as anonline learning model and learn over time when to remind a user at atime that is most likely to get the user to go to bed on time. Forexample, the bedtime reminder may be a system that may remind the userto go to bed 15 minutes before her recommended bedtime on one or moredays. Then the bedtime reminder system may switch behavior and remindthe user 20 minutes before her recommended bedtime for one or more days.It may similarly try different variants, and if one of those variantswas more successful in getting the user to go to bed at the selectedbedtime, then that variant may be adopted. Similarly, learnings aboutthe most successful bedtime reminder variants from other users may beused to set bedtime reminders for users new to the feature. In the moregeneral case, the bedtime reminder model may optimize for some otherbehavior of interest besides just maximizing the probability that theuser will go to bed on time. For example, the system may try to optimizea more general cost function that maximizes engagement, and/or minimizessleep debt accumulation, etc.

Scheduling the bedtime reminder may also be recommended in the morning,based on the sleep state duration data of the user for the previoussleep session. The schedule of the bedtime reminder may be adjusted toaddress accumulating sleep debt. The user may also be congratulatedafter a sleep session in the morning if the user achieved his or herbedtime target. Success may be defined as the user is asleep by herbedtime, and/or defined as the user is relaxed and lying down by herbedtime, attempting to meet the goal even if the user is unable to fallasleep. The bedtime may also be scheduled or recommended by a friend ina challenge form. Consistent bedtimes may also be a household, team, orgroup challenge where all members of the household need to achieve thebedtime in order to succeed.

Providing Feedback Regarding Individual's Sleep Behavior

As part of the present disclosure, determinations may be made from thesleep data as to how the user is performing with respect to the user'sselected bedtime, scheduled waketime, and/or selected sleep duration,and additional GUIs may be generated that include such determinationsand/or include new recommendations for a bedtime, waketime, and/or sleepduration based, at least in part, on such determinations.

In some embodiments, generating the one or more personalized GUIs mayinclude determining, based on the sleep data of the user, how the user'ssleep performance compares with the user's selected or desired sleepperformance. For example, a determination may be made, for each of aplurality of sleep sessions, as to whether or not the user's actualbedtime, actual waketime, sleep state duration, and/or sleep sessionduration were within some acceptable range or threshold of the selectedbedtime, the scheduled waketime, selected sleep duration, and/or thepredicted sleep session duration (which may be based on the target sleepsession duration and which, generally speaking, is equivalent to thetime span bracketed between the selected bedtime and the scheduledwaketime). The user's performance for such sleep sessions, as comparedagainst the selected values, may be displayed on a GUI such as thatshown in FIG. 36, which shows various sleep data points 3601 ontimelines 3604 for the most recent seven days of sleep logs. As can beseen in FIG. 36, the selected bedtimes and the scheduled waketimes maybe indicated with cross-hatched areas 3602 and 3603 that representtolerance bands or thresholds, e.g., ±15 minutes, from the selectedbedtimes and the scheduled waketimes (which would be located, forexample, in the middle of such tolerance bands). The user's sleep datamay be considered to be “on target” when the actual bedtimes and theactual waketimes that the user experiences are within the tolerancebands or thresholds, and such on-target behavior may be indicated usinga graphical indicator, such as goal achievement indicator 3606. Moreinformative timelines 3605 may also be included to show periods ofrestlessness or wakefulness during a sleep session for such days. FIG.37 depicts a different graphical user interface that includes summaryinformation 3702 regarding a single sleep session, including total sleepduration, the actual bedtime, the actual sleep time, how long it tookthe user to fall asleep (based on biometric monitoring device data), andtotal amount of time awake and/or restless during the sleep session.Also displayed is a timeline 3701 indicating which sleep states theperson was in at any given point in the sleep session. In the case wherea user is on-target for one or more of these parameters for a givensleep session, the GUI may be configured to display a congratulatorymessage, e.g., “On target!,” regarding the user's achievement withrespect to that parameter.

If the user is not on-target with respect to one or more of the selectedbedtime, the scheduled waketime, selected sleep duration, and/or thepredicted sleep session duration, then a personalized GUI may begenerated that suggests one or more alternate settings for the selectedbedtime, the scheduled waketime, the selected sleep duration, and/or thepredicted sleep session duration. For example, if the user consistentlymisses attaining the selected sleep duration by 20 minutes, then theuser may be presented with a personalized GUI that suggests that theuser should consider a selected bedtime that is 30 minutes earlier thanthe current setting. In other implementations, the sleep data from otherusers that exhibited a similar inability to meet a similar target sleepmetric but who then adjusted their sleep patterns to meet that metricmay be analyzed to determine which of these parameters typicallyresulted in successful behavior modification of the users when adjusted.For example, in the example above, the sleep logs in the sleep log datastore may be analyzed to locate other users that had similar (within ±15minutes, for example) selected bedtimes, scheduled waketimes, andselected sleep durations and that also consistently missed theirselected sleep durations by approximately 20 minutes (e.g., 20 minutes±10 minutes); the sleep data from these identified users may be furtheranalyzed to determine if they made any adjustments to their selectedbedtime and whether they attained their selected sleep durationsubsequent to such an adjustment. If so, then that adjustment (or arepresentation thereof, e.g., an average of the adjustment) may be usedas the basis for recommending a similar adjustment to the user. Forexample, if such users are identified and analysis indicates that 70% ofthose users re-scheduled their selected bedtime for 30 minutes earlier,on average, than their previous settings, then the user may be providedwith a revised recommended bedtime that is 30 minutes earlier than theircurrent bedtime.

Once a user has been guided through setting a selected sleep durationand a selected bedtime, one or more additional GUIs may be generated toallow the user to easily edit and revise these settings withoutrepeating the more involved guided/coached set up discussed above. Forexample, FIG. 38 depicts a GUI that may be generated to allow a user toeasily change their scheduled waketime 3803, selected bedtime 3802,and/or selected sleep duration 3801, as well as a prompt 3804 to turn onor off a bedtime reminder and another prompt 3805 to allow the user tochange the bedtime reminder timing information. By tapping on any of theindicated settings, the user may be taken to a new GUI (not shown) thatallows for modification of such parameters by the user.

It is to be understood that the GUIs discussed above are merely oneexample of how sleep tracking functionality may be implemented for auser of a biometric monitoring device, and that there may be othertechniques and GUIs that may be used instead that are nonetheless withinthe scope of this disclosure. The GUIs discussed above may also beimplemented in a partial manner, e.g., the GUIs may not include GUIsgeared towards introducing sleep tracking to a new user, but may insteadproceed directly to the setting of a selected bedtime based on a user'saccumulated sleep data (or on other users' sleep data, e.g., if the userhas not accumulated sufficient sleep data of their own yet).

Some aspects of the present disclosure may occur in particular sequencesor orders. For a first example, a determination may be made as towhether a user has a particular number of sleep logs stored in the sleeplog data store (e.g., more than 5 sleep logs). If the user does not havethe particular number of sleep logs, then GUIs associated with a newuser may be generated, such as FIGS. 7 through 17. Once the GUIsassociated with the new user have been generated, the sleep data (asdescribed above) may be obtained and stored in the sleep log data store(as also described above); another determination may also be made as towhether a given number of sleep logs have been stored within a period oftime (e.g., whether more than 5 sleep logs have been stored within 14days).

If the determination is that the requisite number of sleep logs havebeen gathered, then additional determinations may be made as to whetherthe user has edited a selected sleep duration (i.e., sleep goal),whether the user has more a specific number of sleep logs within aperiod of time (e.g., more than 5 sleep logs within 14 days), and/orwhether the user's sleep state duration data indicates that the user iswithin a specific threshold of the selected sleep duration. If the userhas not edited this sleep goal, does not have more than the requisitenumber of sleep logs within the period of time, and/or the user's sleepstate duration data does not indicate that the user is within thespecific threshold, then GUIs associated with selecting a selected sleepduration, waketime, waketime alarm, recommended bedtime, and/or bedtimereminder may be generated. For instance, this may include FIGS. 18-28and 31-34. However, if one or more of the determinations are opposite,then different GUIs may be generated, such as GUIs associated withselecting a waketime, waketime alarm, recommended bedtime, and/orbedtime reminder.

It should be noted that each of the GUIs described herein and shown isnot an exhaustive list of the GUIs that may be generated as part of thisdisclosure. Furthermore, each and every feature described and shown maybe included in one or more of the other GUIs; such features andinformation in the GUIs are not mutually exclusive of each and everyother feature and information.

It is also to be understood that the techniques discussed above may beimplemented as methods, as well as systems or devices configured, usingcomputer-executable instructions stored in memory, so as to cause one ormore processors to perform such methods. It is to be further understoodthat such computer-executable instructions may also be implemented innon-transitory form as computer-executable instructions that are storedon a storage device, e.g., in a computer-readable memory.

Importantly, the present invention is neither limited to any singleaspect nor implementation, nor to any combinations and/or permutationsof such aspects and/or implementations. Moreover, each of the aspects ofthe present invention, and/or implementations thereof, may be employedalone or in combination with one or more of the other aspects and/orimplementations thereof. For the sake of brevity, many of thosepermutations and combinations will not be discussed and/or illustratedseparately herein.

Embodiments discussed herein may provide solutions to a number oftechnical problems. For example, embodiments that generate suggestedsleep-tracking parameters solve the technical problem of providing ameaningful user interface to a user. Such may be the case because thoseembodiments may generate a user interface with information that isrelevant to the user, rather than provide default values thatcharacterize, say, a general population. As described above, theseparameters may include suggested waketime, sleep duration, bedtime, andthe like. Thus, such embodiments may provide comparatively accurateinformation and suggestions to a user base that includes users with awide variety of different sleep habits and needs.

Embodiments discussed herein may also provide solutions for increasing auser's engagement with a fitness monitor that tracks sleep. For example,embodiments may determine when to trigger the presentation orintroduction of a sleep-related tracking feature. Such may be the casewhen embodiments selectively determine when to introduce a sleepconsistency goal. In some cases, an embodiment may first introduce asleep duration goal and then, after a determinable period (as may bedetermined based on historical sleep logs, for example), introduce asleep consistency goal, e.g., to promote or encourage consistentbedtimes and waketimes, on-top of the sleep duration goal. Thus, suchembodiments may provide comparative improvements for systems to retainand engage a user base compared, for example, with systems that do notprovide such a staged or gradual introduction to sleep consistencytracking.

1. (canceled)
 2. A method of generating one or more graphical user interfaces for setting a sleep schedule of a user of a biometric monitoring device, the method comprising: determining that an account assigned to the user is associated with less than a first number of sleep logs in a sleep log data store, wherein each sleep log includes sleep data derived from sensor data generated by the biometric monitoring device for a sleep session, the sleep data for each sleep log specifying various sleep states of the user for that respective sleep session; causing, responsive, at least in part, to the determination that the account assigned to the user is associated with less than the first number of sleep logs in the sleep log data store, a first set of one or more graphical user interfaces to be displayed to the user, wherein at least one graphical user interface of the first set of one or more graphical user interfaces to be displayed to the user includes a sleep duration user interface element configured to allow the user to specify a selected sleep duration of the user for one or more first future sleep sessions of the user, and the first set of one or more graphical user interfaces lack a scheduled waketime user interface element configured to allow the user to specify a scheduled waketime in association with the selected sleep duration of the user; obtaining the selected sleep duration specified by the user; obtaining sleep data for one or more sleep sessions of the user, wherein the sleep data for each sleep session includes sleep state duration data representative of the total amount of time the user spent in a subset of non-awake sleep states during that sleep session; storing the sleep data in the sleep log data store as one or more sleep logs associated with the account assigned to the user; and comparing the sleep state duration data for one or more of the sleep logs stored in the sleep log data store with the selected sleep duration.
 3. The method of claim 2, wherein causing the first set of one or more graphical user interfaces to be displayed further includes: causing a suggested sleep duration to be displayed in a graphical user interface element of at least one graphical user interface of the first set of one or more graphical user interfaces, wherein the suggested sleep duration is based on one or more of: a default value and a target sleep duration determined based on one or more of sleep logs stored in the sleep log data store and including sleep data derived from sensor data generated by other biometric monitoring devices of other users.
 4. The method of claim 3, further comprising: obtaining, after the display of the suggested sleep duration, a second selected sleep duration specified by the user.
 5. The method of claim 2, further comprising: determining that the selected sleep duration is not within a threshold amount of a default sleep duration value; and causing, based on the determination that the selected sleep duration is not within the threshold amount of the default sleep duration value, a suggested sleep duration to be displayed in a graphical user interface element of at least one graphical user interface of a second set of the one or more graphical user interfaces, wherein the suggested sleep duration is based on one or more of: a default value and a target sleep duration determined based on one or more sleep logs stored in the sleep log data store and including sleep data derived from sensor data generated by other biometric monitoring devices of other users.
 6. The method of claim 5, further comprising: obtaining a second selected sleep duration after the display of the suggested sleep duration.
 7. The method of claim 2, wherein the determining that the account assigned to the user is associated with less than the first number of sleep logs in the sleep log data store is evaluated based on sleep logs associated with the account assigned to the user and associated with sleep sessions that occurred within a first period of time.
 8. The method of claim 2, wherein causing the first set of one or more graphical user interfaces to be displayed further includes: causing information associated with average recommended sleep durations of a group of people to be displayed in a graphical user interface element of at least one graphical user interface of the first set of one or more graphical user interfaces.
 9. The method of claim 2, wherein causing the first set of one or more graphical user interfaces to be displayed further includes causing information associated with the selected sleep duration, other than the selected sleep duration, to be displayed in a graphical user interface element of at least one graphical user interface of the first set of one or more graphical user interfaces.
 10. The method of claim 2, wherein causing the first set of one or more graphical user interfaces to be displayed further includes: causing the display of sleep state duration data of the user for at least one of the one or more sleep sessions.
 11. The method of claim 2, further comprising: determining, after the obtaining sleep data for one or more sleep sessions of the user and the storing the sleep data in the sleep log data store, that the account assigned to the user is associated with at least one or more of: a second number of sleep logs in the sleep log data store and a second time period elapsed since the selected sleep duration was obtained; and causing, in response to the determination that the account assigned to the user is associated with at least one or more of: the second number of sleep logs in the sleep log data store and the second time period elapsed since the selected sleep duration was obtained, a second set of one or more graphical user interfaces to be displayed to the user.
 12. The method of claim 11, wherein causing the second set of one or more graphical user interfaces to be displayed further includes: calculating, in response to a determination that the sleep state duration data of the user for one or more of the one or more sleep sessions is outside a threshold amount from the selected sleep duration, a recommended sleep duration, wherein the recommended sleep duration is based on sleep data in sleep logs for one or more users that are stored in the sleep log data store, and causing the recommended sleep duration to be displayed in a graphical user interface element of at least one graphical user interface of the second set of one or more graphical user interfaces.
 13. The method of claim 12, wherein the causing the recommended sleep duration to be displayed in a graphical user interface element of at least one graphical user interface of the second set of one or more graphical user interfaces includes: causing an incremented recommended sleep duration to be displayed in a graphical user interface element in each of a series graphical user interfaces of the second set of one or more graphical user interfaces, wherein each incremented recommended sleep duration is greater than the previously displayed incremented recommended sleep duration, and wherein each incremented recommended sleep duration is associated with a different future sleep session.
 14. The method of claim 12, further comprising: obtaining a second selected sleep duration after causing the recommended sleep duration to be displayed.
 15. The method of claim 12, wherein the recommended sleep duration is further based on one or more of: a look-up table, demographics of the user, one or more specific days of the week, a specific time of year, holidays, workdays of the user, non-workdays of the user, a seasonal time change, a geographic location, travel by the user between at least two time zones, exercise of the user, and a duration of daylight in a day.
 16. The method of claim 11, further comprising: obtaining a scheduled waketime of the user for one or more second future sleep sessions of the user after determining that the account assigned to the user is associated with at least the second number of sleep logs in the sleep log data store.
 17. The method of claim 16, wherein causing the second set of one or more graphical user interfaces to be displayed further includes: calculating, after determining that the account assigned to the user is associated with at least a third number of sleep logs in the sleep log data store, a recommended waketime based on waketimes derived from the sleep data associated with the user stored in the sleep log data store, and causing the recommended waketime to be displayed in a graphical user interface element of at least one graphical user interface of the second set of one or more graphical user interfaces.
 18. The method of claim 16, wherein causing the second set of one or more graphical user interfaces to be displayed further includes: obtaining a recommended bedtime based on a calculation accounting for, at least in part, the scheduled waketime and a sleep efficiency based on sleep data associated with one or more users stored in the sleep log data store, and causing the recommended bedtime to be displayed in a graphical user interface element of at least one graphical user interface of the second set of one or more graphical user interfaces.
 19. The method of claim 18, wherein: the calculation on which the recommended bedtime is based further accounts for, at least in part, the selected sleep duration of the user.
 20. The method of claim 18, wherein: obtaining the recommended bedtime is further based on a recommended waketime that is used as the scheduled waketime.
 21. The method of claim 18, wherein: the sleep efficiency is representative, at least in part, of a correlation between sleep state duration data for one or more sleep sessions associated with one or more sleep logs stored in the sleep log data store and sleep session duration data for the corresponding plurality of sleep sessions, the sleep state duration data for each sleep session is representative of the total amount of time the user that is associated with the sleep session spent in a subset of non-awake sleep states during that sleep session, and the sleep session duration data for each sleep session is representative of the total duration of that sleep session.
 22. The method of claim 18, further comprising: obtaining timing information indicating one or more selected reminder times for a bedtime reminder, and generating the bedtime reminder based on the timing information.
 23. The method of claim 22, wherein the bedtime reminder includes information regarding the recommended bedtime.
 24. The method of claim 22, wherein causing the second set of one or more graphical user interfaces to be displayed further includes: calculating a recommended reminder time for the bedtime reminder based on the recommended bedtime, and causing the recommended reminder time to be displayed in a graphical user interface element of at least one graphical user interface of the second set of one or more graphical user interfaces.
 25. The method of claim 22, further comprising: obtaining day information indicating one or more selected reminder days for the bedtime reminder, wherein the generating of the bedtime reminder is further based on the day information.
 26. The method of claim 22, wherein: the generating the bedtime reminder includes providing a notification, and the notification is one or more of: a message, an auditory output, an electronic communication, an electromagnetic communication, a visual output, and a tactile output.
 27. The method of claim 12, further comprising: causing, based on a determination that the sleep state duration data of the user is not within a threshold amount of the selected sleep duration, one or more of: a second recommended bedtime, a recommended sleep duration, and a recommended waketime to be displayed in a graphical user interface element of at least one graphical user interface of a third set of one or more graphical user interfaces.
 28. The method of claim 2, further comprising: determining whether the sleep state duration data of the user for at least one of the one or more sleep sessions of the user is within a threshold amount of the selected sleep duration, and causing information associated with the determination of whether the sleep state duration data of the user for one of the sleep sessions of the user is within the threshold amount of the selected sleep duration to be displayed in a graphical user interface element of at least one graphical user interface of a second set of one or more graphical user interfaces.
 29. The method of claim 28, wherein the causing the information associated with the determination of whether the sleep state duration data of the user for one of the sleep sessions of the user is within the threshold amount of the selected sleep duration to be displayed includes: causing a timeline to be displayed showing a graphical indication of the threshold amount relative to the timeline and a graphical indication of the sleep state duration data relative to the timeline.
 30. The method of claim 2, wherein causing the first set of one or more graphical user interfaces to be displayed further includes: causing instructional information about using the biometric monitoring device to be displayed. 